1. A method for generating physiological embeddings for reduced computational overhead, the method comprising: receiving, from sensors, physiological signals corresponding to a plurality of modalities; time-aligning the physiological signals to produce synchronized signals; preprocessing the synchronized signals by executing modality-aware signal-conditioning operation to produce preprocessed signals; encoding windowed segments of the preprocessed signals to generate a shared embedding for the windowed segments; storing the shared embedding in a machine-readable store; and exposing the shared embedding for consumption by one or more downstream computational processes.

2. The method of claim 1, wherein the plurality of modalities comprise at least two of ballistocardiogram (BCG), electrocardiogram (ECG), and photoplethysmogram (PPG).

3. The method of claim 1, wherein time-aligning comprises associating the physiological signals with acquisition timestamps and estimating and compensating for inter-sensor clock drift.

4. The method of claim 1, wherein preprocessing comprises at least one of band-pass filtering with modality-aware frequency selection and wavelet-domain denoising with adaptive thresholds.

5. The method of claim 1, wherein encoding comprises using a streaming encoder having an attention mechanism and/or a convolutional mechanism with a bounded temporal receptive field to provide deterministic latency during inference.

6. The method of claim 1, further comprising representing each shared embedding in a quantized format and storing quantization parameters associated with the shared embedding.

7. The method of claim 1, wherein storing comprises writing the shared embedding into a circular memory structure and batching uploads according to a time-to-live (TTL) policy while verifying an integrity check prior to transmission.

8. The method of claim 1, further comprising training the encoder using one or more of masked-segment modeling, cross-modal prediction, and contrastive alignment to learn generalized physiological representations.

9. The method of claim 1, further comprising implementing out-of-distribution detection by computing a distance measure in an embedding space and suppressing outputs when current embeddings exceed learned distribution boundaries.

10. The method of claim 1, wherein exposing the shared embedding comprises providing an interface that receives streaming multi-channel frames with associated metadata and outputs per-window embedding vectors with optional quantization parameters and quality indicators.

11. The method of claim 1, wherein time-aligning and/or preprocessing comprise establishing a common temporal reference across modalities using at least one of resampling, attention-based alignment with learned temporal offsets, differentiable time-warping, or latent-space interpolation.

12. The method of claim 1, further comprising operating in a single-modality mode responsive to unavailable or low-quality modalities, wherein masked inputs preserve streaming operation of the encoder.

13. The method of claim 1, further comprising retrieving time-keyed embeddings using similarity search to surface physiologically similar epochs for trend analysis, baseline comparison, or cohorting.

14. The method of claim 1, further comprising generating a control signal from the shared embedding and actuating at least one of a respiratory support device, a bed actuator, an HVAC controller, a lighting controller, a haptic output device, and a notification manager.

15. A system comprising at least one processor and a memory storing instructions that, when executed by the at least one processor, cause the system to: receive physiological signals from one or more sensors; time-align the physiological signals to produce synchronized signals; preprocess the synchronized signals by executing at least one modality-aware signal-conditioning operation to produce preprocessed signals; encode windowed segments of the preprocessed signals to generate a shared embedding for the windowed segments; store the shared embedding in a machine-readable store; and expose the shared embedding for consumption by one or more downstream computational processes.

16. The system of claim 15, wherein the instructions further cause the system to associate the physiological signals with hardware timestamps and estimate and compensate inter-sensor clock drift.

17. The system of claim 15, wherein the instructions further cause the system to perform accelerometer-gated motion artifact handling configured to suppress noise during detected movement.

18. The system of claim 15, wherein the encoder is configured for streaming inference with attention and/or convolution having a bounded temporal receptive field.

19. The system of claim 15, wherein the instructions further cause the system to evaluate a candidate model in shadow against a production model and to initiate rollback responsive to a violation of performance criteria.

20. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: receive, from one or more sensors, physiological signals from one or more modalities; time-align the physiological signals to produce synchronized signals; preprocess the synchronized signals by executing at least one modality-aware signal-conditioning operation to produce preprocessed signals; encode windowed segments of the preprocessed signals to generate a shared embedding for the windowed segments; store the shared embedding in a machine-readable store; and expose the shared embedding for consumption by one or more downstream computational processes.

Description:SELF-SUPERVISED MULTIMODAL BIOSIGNAL REPRESENTATION LEARNING SYSTEM
FIELD OF INVENTION
[0001] The present disclosure relates to multimodal biosignal processing systems using self-supervised machine learning, and more particularly to a system that generates embeddings from physiological signals for continuous physiological monitoring applications.
BACKGROUND
[0002] Physiological monitoring systems face computational challenges in processing multimodal biosignals from heterogeneous sensors with different sampling rates, noise characteristics, and temporal alignment requirements. Traditional approaches require separate processing pipelines for each signal modality, leading to increased memory consumption, processing latency, and system complexity when attempting to extract meaningful physiological patterns from synchronized ballistocardiogram (BCG), electrocardiogram (ECG), and photoplethysmogram (PPG) data streams. The technical problem involves generating unified representations that capture cross-modal physiological correlations while maintaining real-time processing constraints on resource-limited edge computing platforms.
[0003] Traditional solutions typically employ task-specific supervised learning models that process individual signal modalities in isolation, requiring extensive labeled datasets and separate training procedures for each clinical application such as respiratory rate estimation, sleep staging, or anomaly detection. These systems implement fixed feature extraction algorithms followed by dedicated classification networks, resulting in computational redundancy and inability to leverage shared physiological patterns across different monitoring tasks. Existing multimodal fusion approaches often rely on simple concatenation or weighted averaging of features extracted independently from each signal type, without accounting for temporal dependencies and cross-modal correlations that characterize physiological processes.
[0004] These conventional approaches suffer from several technical limitations including high memory overhead due to maintaining separate model parameters for each task, increased inference latency from sequential processing of multiple specialized networks, and poor generalization performance when signal quality degrades or sensor configurations change. The lack of shared representations prevents efficient transfer learning between related physiological monitoring tasks, while the dependence on supervised training limits scalability due to the high cost of obtaining expert-annotated physiological data. Additionally, existing systems struggle with temporal synchronization across sensors with different hardware characteristics and sampling frequencies, leading to alignment errors that degrade the accuracy of cross-modal pattern recognition and limit the effectiveness of multimodal physiological analysis.
SUMMARY
[0005] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
[0006] According to an aspect of the present disclosure, a method for generating physiological embeddings for reduced computational overhead may be provided. The method may comprise receiving, from sensors, physiological signals corresponding to a plurality of modalities. The method may time-align the physiological signals to produce synchronized signals. The method may preprocess the synchronized signals by executing modality-aware signal-conditioning operations to produce preprocessed signals. The method may encode windowed segments of the preprocessed signals to generate a shared embedding for the windowed segments. The method may store the shared embedding in a machine-readable store. The method may expose the shared embedding for consumption by one or more downstream computational processes.
[0007] According to other aspects of the present disclosure, the computer-implemented method may include one or more of the following features. The plurality of modalities may comprise at least two of ballistocardiogram (BCG), electrocardiogram (ECG), and photoplethysmogram (PPG). The time-aligning may comprise associating the physiological signals with acquisition timestamps and estimating and compensating for inter-sensor clock drift. The preprocessing may comprise at least one of band-pass filtering with modality-aware frequency selection and wavelet-domain denoising with adaptive thresholds. The encoding may comprise using a streaming encoder having an attention mechanism and/or a convolutional mechanism with a bounded temporal receptive field to provide deterministic latency during inference. Pre-training uses a bi-directional encoder. Streaming with a bounded receptive field is an optional deployment variant for inference. The method may further comprise representing each shared embedding in a quantized format and storing quantization parameters associated with the shared embedding. The storing may comprise writing the shared embedding into a circular memory structure and batching uploads according to a time-to-live (TTL) policy while verifying an integrity check prior to transmission.
[0008] According to another aspect of the present disclosure, a system comprising at least one processor and a memory storing instructions may be provided. When executed by the at least one processor, the instructions may cause the system to receive physiological signals from one or more sensors. The instructions may cause the system to time-align the physiological signals to produce synchronized signals. The instructions may cause the system to preprocess the synchronized signals by executing at least one modality-aware signal-conditioning operation to produce preprocessed signals. The instructions may cause the system to encode windowed segments of the preprocessed signals to generate a shared embedding for the windowed segments. The instructions may cause the system to store the shared embedding in a machine-readable store and expose the shared embedding for consumption by one or more downstream computational processes.
[0009] According to other aspects of the present disclosure, the system may include one or more of the following features. The instructions may further cause the system to associate the physiological signals with hardware timestamps and estimate and compensate inter-sensor clock drift. The instructions may further cause the system to perform accelerometer-gated motion artifact handling configured to suppress noise during detected movement. The encoder may be configured for streaming inference with attention and/or convolution having a bounded temporal receptive field. The instructions may further cause the system to evaluate a candidate model in shadow against a production model and to initiate rollback responsive to a violation of performance criteria.
[0010] According to another aspect of the present disclosure, a non-transitory computer-readable medium storing instructions may be provided. When executed by one or more processors, the instructions may cause the one or more processors to receive, from one or more sensors, physiological signals from one or more modalities. The instructions may cause the processors to time-align the physiological signals to produce synchronized signals. The instructions may cause the processors to preprocess the synchronized signals by executing at least one modality-aware signal-conditioning operation to produce preprocessed signals. The instructions may cause the processors to encode windowed segments of the preprocessed signals to generate a shared embedding for the windowed segments. The instructions may cause the processors to store the shared embedding in a machine-readable store. The instructions may cause the processors to expose the shared embedding for consumption by one or more downstream computational processes.
[0011] According to other aspects of the present disclosure, the instructions stored on the non-transitory computer-readable medium may cause execution of one or more of the following features. The one or more modalities may comprise at least two of ballistocardiogram, electrocardiogram, and photoplethysmogram. The encoding may comprise using a streaming encoder having an attention mechanism and/or a convolutional mechanism with a bounded temporal receptive field to provide deterministic latency during inference.
[0012] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive.
BRIEF DESCRIPTION OF FIGURES
[0013] Non-limiting and non-exhaustive examples are described with reference to the following figures.
[0014] FIG. 1 illustrates a flowchart for a computer-implemented method for self-supervised multimodal biosignal representation learning, according to aspects of the present disclosure.
[0015] FIG. 2 depicts a flowchart for a self-supervised multimodal biosignal representation learning method with error handling, according to aspects of the present disclosure.
[0016] FIG. 3 illustrates a flowchart for a computer-implemented method with conditional encoder training logic, according to aspects of the present disclosure.
[0017] FIG. 4 depicts a flowchart for a comprehensive monitoring workflow method with quality assessment, according to aspects of the present disclosure.
[0018] FIG. 5 illustrates a block diagram of a multimodal biosignal processing system with interconnected modules, according to aspects of the present disclosure.
[0019] FIG. 6A illustrates example multimodal physiological waveforms prior to time-alignment, according to aspects of the present disclosure.
[0020] FIG. 6B illustrates the example multimodal physiological waveforms of FIG. 6A after time-alignment, according to aspects of the present disclosure.
DETAILED DESCRIPTION
[0021] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "acquiring," "aligning," "encoding," "storing," "providing," "training," and other forms thereof, are intended to be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any system and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, system and methods are now described.
[0022] The disclosed embodiments are merely examples of the disclosure, which may be embodied in various forms. Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments described but is to be accorded the widest scope consistent with the principles and features described herein.
[0023] Unless expressly recited in the claims, any numerical values, ranges, sampling frequencies, window lengths, mask spans, kernel sizes, or performance targets described herein are presented as non-limiting examples. Equivalent alternatives, supersets, or subsets are contemplated, and implementations may vary according to hardware, firmware, software, or clinical context without departing from the scope of the invention.
[0024] Processing multimodal biosignals from heterogeneous sensors presents computational challenges related to temporal synchronization, noise characteristics, and sampling rate variations across different physiological measurement modalities. Ballistocardiogram (BCG) sensors may operate at sampling frequencies ranging from 100 to 1000 Hz and may be implemented using various sensing technologies including piezoelectric sensors that detect mechanical vibrations through charge generation, pneumatic sensors that measure pressure variations in air-filled chambers, optical sensors utilizing bend fiber technology that detect mechanical deformation through light transmission changes, accelerometer-based sensors that measure body movement and cardiac recoil forces, and load cell sensors that detect weight distribution changes on bed surfaces. Electrocardiogram (ECG) sensors may utilize sampling rates between 250 and 2000 Hz, and photoplethysmogram (PPG) sensors may employ sampling frequencies from 25 to 500 Hz. Traditional approaches require separate processing pipelines for each signal modality, leading to increased memory consumption exceeding 2 GB for concurrent processing of multiple data streams, processing complexity arising from independent feature extraction algorithms applied to each biosignal type, and difficulty in capturing cross-modal physiological relationships.
[0025] Current solutions employ task-specific supervised learning models processing individual signal modalities in isolation, requiring extensive labeled datasets containing thousands of annotated physiological events for each clinical application. Cardiac arrhythmia detection models may require separate training datasets with manually annotated abnormal heart rhythms, sleep apnea detection algorithms may demand polysomnography recordings with expert-labeled respiratory events, and heart failure monitoring systems may necessitate curated datasets with clinically validated cardiac dysfunction patterns. Each task-specific model consumes dedicated computational resources during training and inference, with memory requirements ranging from 500 MB to 2 GB per model, making it difficult to deploy comprehensive physiological monitoring systems that can address multiple clinical conditions simultaneously.
[0026] Existing multimodal fusion approaches rely on simple concatenation or weighted averaging of features extracted independently from each signal type without accounting for temporal dependencies and cross-modal correlations present in synchronized physiological measurements. Feature concatenation methods may combine time-domain statistics, frequency-domain coefficients, and morphological parameters extracted separately from BCG, ECG, and PPG signals, resulting in high-dimensional feature vectors with dimensions exceeding 1000 elements. Weighted averaging approaches may apply fixed or learned weights to individual modality features, but these methods fail to capture dynamic relationships between physiological signals that vary with cardiac cycle phases, respiratory patterns, and autonomic nervous system activity.
[0027] The technical solution addresses these limitations through hardware-timestamped synchronization achieving temporal alignment accuracy of less than 5 milliseconds between multimodal biosignals using precision time protocol (PTP) or connection interval tagging mechanisms. Modality-specific preprocessing may include denoising to reduce sensor and environmental noise while preserving physiologic content, for example by applying wavelet-based denoising (e.g., wavelet transform filtering with thresholding) to one or more modalities prior to embedding generation. In some embodiments, denoising parameters are adaptively selected based on statistics computed over an analysis window to balance noise suppression and preservation of clinically relevant signal components.
[0028] A definitions section is provided for clarity. As used herein, a 'shared embedding' refers to a learned vector representation produced from synchronized biosignals and suitable for multiple downstream tasks without task-specific feature engineering. A 'windowed segment' refers to a contiguous segment of input signals processed with overlap to enable comprehensive analysis. An 'out-of-distribution (OOD) gate' refers to inference-time logic that estimates whether current embeddings deviate from learned distributions and conditionally adapts output or resource policies.
[0029] A transformer encoder processes windowed segments of one or more modalities using attention mechanisms to generate shared embeddings that represent underlying physiological activity. The transformer architecture models temporal dependencies to capture physiologic patterns at multiple time scales and to integrate information across modalities. In some embodiments, the attention mechanism (e.g., multi-head self-attention and/or cross-attention) enables the encoder to emphasize relevant temporal relationships and cross-modal correlations when forming the embeddings. The resulting embeddings may be represented in a compact latent space and may optionally be compressed, quantized, or otherwise formatted for storage or transmission depending on implementation constraints. Various architectural choices and computational optimizations may be employed to meet resource and latency targets without limiting the encoder to any particular embedding dimensionality, numeric precision, or specific attention-head partitioning technique.
[0030] The transformer encoder implements self-supervised training to learn generalized physiological representations from unlabeled multimodal biosignal data. The transformer encoder utilizes masked segment modeling with variable mask spans ranging from 20 milliseconds to 10 seconds duration, where masked portions of input signals are reconstructed using contextual information from unmasked segments, enabling the transformer encoder to learn temporal dependencies and signal morphology patterns without requiring labeled training data. Cross-modal prediction implements cycle-consistent loss functions as an example training objective for bidirectional signal prediction between modalities, where the encoder learns to predict ECG features from PPG and BCG inputs and vice versa, capturing physiological relationships between BCG cardiac mechanical activity, ECG electrical activity, and PPG vascular volume changes. Contrastive alignment utilizes InfoNCE loss as another example training objective with temperature scaling parameters to align embeddings from temporally synchronized signal segments while separating embeddings from temporally distant or physiologically dissimilar segments, training the encoder to generate embedding representations that capture physiological state variations across different time periods and signal conditions.
[0031] The system reduces memory overhead by sharing learned representations across multiple downstream tasks, decreasing total memory consumption from multiple gigabytes required for separate task-specific models to hundreds of megabytes for a single shared embedding model with lightweight task heads. Processing efficiency improves through unified processing of multimodal inputs in a single forward pass, eliminating sequential processing delays associated with independent modality-specific pipelines. Generalization performance improves through self-supervised pretraining on large unlabeled datasets containing a plurality of signal segments, enabling the transformer encoder to learn robust physiological representations without requiring extensive manual annotation for each target application.
[0032] Efficient transfer learning between related physiological monitoring tasks occurs through fine-tuning of shared embeddings with task-specific heads, reducing training time and data requirements for new clinical applications. Temporal synchronization across sensors with different hardware characteristics and sampling frequencies is addressed through hardware timestamping and drift compensation algorithms that account for clock skew, jitter, and sampling rate variations, enabling coherent multimodal analysis of physiological signals acquired from heterogeneous sensor systems deployed in clinical measurement equipment, wearable devices, and bed-based monitoring systems.
[0033] The following figures illustrate different aspects and embodiments of the multimodal biosignal representation learning system, where each flowchart emphasizes particular implementation features while sharing common core processing steps. FIG. 1 presents the fundamental method for generating physiological embeddings, establishing the basic processing pipeline from signal acquisition through embedding exposure. FIG. 2 extends this foundation by incorporating error handling mechanisms and retry logic to ensure robust operation under varying signal quality conditions. FIG. 3 demonstrates conditional training scenarios where the system may utilize either pre-trained models or perform self-supervised training based on deployment requirements and available computational resources. FIG. 4 illustrates a comprehensive monitoring workflow that integrates quality assessment procedures and adaptive preprocessing strategies for deployment scenarios. While these methods share common processing stages including signal acquisition, synchronization, preprocessing, encoding, and storage, each flowchart represents a different embodiment that addresses specific operational requirements, error conditions, or deployment constraints that may be encountered in physiological monitoring applications.
[0034] Referring to FIG. 1, a computer-implemented method 100 for generating physiological embeddings provides a systematic approach for processing physiological signals from multiple sensor modalities. The method 100 begins with step 102, where physiological signals are received from one or more sensors. The acquisition process may involve collecting signals from various sensor types including, by way of example, ballistocardiogram (BCG) sensors measuring cardiac mechanical activity through body movement detection using piezoelectric transducers that convert mechanical vibrations into electrical signals, pneumatic sensors that detect pressure variations in air-filled chambers positioned beneath mattresses, optical sensors utilizing bend fiber technology that measure mechanical deformation through changes in light transmission properties, accelerometer-based sensors that capture body movement and cardiac recoil forces, and load cell sensors that detect weight distribution changes on bed surfaces. Additional sensors may include electrocardiogram (ECG) sensors capturing electrical cardiac activity from chest-mounted electrodes, photoplethysmogram (PPG) sensors monitoring blood volume changes through optical measurements from wearable devices, accelerometer sensors detecting motion and orientation changes, temperature sensors monitoring skin or ambient temperature variations, impedance sensors measuring bioelectrical impedance for body composition analysis, and pulse oximetry sensors measuring blood oxygen saturation levels. The one or more sensors may be configured to operate during physiological monitoring sessions, with each sensor type providing complementary information about cardiovascular, respiratory, and other physiological processes.
[0035] The method 100 proceeds to step 104, where the physiological signals are time-aligned to produce synchronized signals. The time-alignment process may implement precision time protocol (PTP) or connection interval tagging to achieve temporal alignment accuracy of less than 5 milliseconds between the multimodal biosignals. PTP synchronization may utilize network-based clock distribution mechanisms to maintain microsecond-level timing accuracy across distributed sensor nodes, while connection interval tagging may leverage connection event timestamps to align signals acquired from wireless devices. Drift compensation algorithms may account for clock skew accumulation over extended monitoring periods, applying linear or polynomial correction models to maintain temporal coherence between signals acquired from sensors with different crystal oscillator characteristics and temperature dependencies.
[0036] An illustrative example of a time-alignment implementation is now described. Hardware timestamping may be implemented using BLE connection interval tagging or PTP synchronization on a Wi-Fi hub, where each sensor node captures precise timing information at the moment of signal acquisition. Skew compensation may utilize a linear drift model that is updated periodically based on accumulated timing measurements across multiple analysis windows. The hardware-timestamped, drift-compensated multimodal synchronization may achieve alignment error of less than 5 milliseconds between sensor modalities, reducing cross-modal reconstruction loss during self-supervised training. The time-alignment procedure may comprise capturing hardware timestamps at signal acquisition, estimating inter-sensor timing offsets using correlation-based alignment or connection event synchronization, fitting a drift model using linear or polynomial regression over accumulated timing measurements, resampling signals to a common temporal reference frame using interpolation techniques, and managing synchronized samples in ring buffers with fixed memory allocation to prevent unbounded memory growth. Missing-modality handling may utilize a learned imputation head only when confidence exceeds a predetermined threshold, and otherwise the modality may be dropped for that analysis window to prevent degradation of embedding quality from unreliable signal estimates.
[0037] FIG. 6A illustrates representative segments of multiple physiological signals acquired over a common observation interval prior to time synchronization and/or computational time-alignment. The plotted waveforms include an electrocardiogram (ECG), a ballistocardiogram (BCG) signal measured from an under-mattress sensor, a BCG signal measured from a chest-worn sensor, and a photoplethysmogram (PLETH) signal. Vertical dashed lines indicate nominal beat markers (e.g., ECG R-peak occurrences or other reference events) used as a timing reference. As shown, corresponding mechanical and optical features in the BCG and PLETH signals (e.g., characteristic deflections associated with cardiac ejection and peripheral pulse upstrokes) are not consistently coincident with the reference markers across modalities. This mis-coincidence can result from clock offset, drift, differing sample clocks, transport latency, buffering, or differing sensor-to-sensor pipeline delays, and motivates subsequent synchronization and time-alignment processing.
[0038] FIG. 6B illustrates the same (or similar) multi-signal interval after applying time synchronization and/or time-alignment processing to compensate for inter-sensor timing errors. In the depicted example, the vertical dashed lines again denote the reference beat markers, and the ECG, under-mattress BCG, chest BCG, and PLETH waveforms exhibit improved temporal correspondence relative to FIG. 6A. In particular, salient physiological features (e.g., BCG complexes and PLETH pulse onsets) occur in a more repeatable temporal relationship to the reference markers, indicating that offsets and/or drift between modalities have been reduced. Such alignment may be achieved using network or device timing (e.g., precision clock synchronization) and/or algorithmic alignment (e.g., connection-event tagging, cross-correlation, feature-based matching, and drift-compensation models), thereby producing synchronized signals suitable for downstream fusion, beat-to-beat feature extraction, and multimodal analytics.
[0039] With continued reference to FIG. 1, the method 100 advances to step 106, where the synchronized signals are preprocessed using modality-aware signal-conditioning operations to produce preprocessed signals. The preprocessing operations may include modality-specific filtering selected to preserve clinically relevant morphology while attenuating noise and artifacts, for example, band-pass filtering of ECG signals to reduce baseline wander and high-frequency interference while preserving QRS complex morphology, band-pass filtering of PPG signals to retain pulsatile components while mitigating motion-related contamination, and filtering of BCG signals tailored to cardiac mechanical signal characteristics. In some embodiments, the preprocessing may also include additional denoising operations, such as wavelet-based denoising and/or other time-frequency or adaptive filtering techniques, with the selection of denoising approach and associated parameters varying based on modality, sensor configuration, and operating conditions.
[0040] The preprocessing operations may incorporate robust scaling and outlier clipping specifically applied to BCG signals as part of the normalization process. Robust scaling may utilize median and interquartile range statistics instead of mean and standard deviation to reduce sensitivity to outlier values commonly present in mechanical cardiac measurements, while outlier clipping may remove signal segments exceeding predetermined amplitude thresholds based on physiological plausibility constraints. The modality-aware signal-conditioning operations may further comprise accelerometer-gated filtering module configured to suppress noise during detected movement periods exceeding an acceleration magnitude threshold, where accelerometer data provides contextual information about patient movement that may contaminate physiological signal measurements.
[0041] As further shown in FIG. 1, the method 100 continues to step 108, where windowed segments of the preprocessed signals are encoded to generate a shared embedding for the windowed segments. The encoding process may be implemented using an encoder, such as a transformer-based neural network, configured to receive windowed segments of the synchronized, preprocessed signals and to output one or more shared embeddings representing physiologic activity reflected in the multimodal inputs. The encoder may be arranged to model temporal dependencies within each modality and to fuse information across modalities such that the embeddings capture physiologic patterns occurring over multiple time scales. In some embodiments, the window duration and hop interval are selectable (e.g., fixed or adaptive) to provide sufficient temporal context for extracting features relevant to the intended downstream tasks. The encoder may employ attention mechanisms, including self-attention and/or cross-attention, to selectively weight temporally separated portions of a signal and/or to emphasize correlations between modalities when forming the embeddings. The particular architecture, internal parameterization, and computational implementation of the encoder may vary across embodiments, and may be selected based on performance targets and resource constraints, without limitation to any specific optimization technique or attention-head arrangement.
[0042] The encoding process may incorporate self-supervised training of the transformer encoder to learn generalized physiological representations from unlabeled multimodal biosignal data. In some embodiments, the transformer encoder includes one or more transformer layers with attention mechanisms configured to process temporal sequences of preprocessed physiological signals and to generate embedding representations that capture temporal structure and cross-modal physiological patterns.
[0043] During self-supervised training, the encoder may be optimized using one or more self-supervised objectives (non-limiting examples of which are described below) to encourage the embeddings to retain information that is predictive of physiologic state. For example, masked segment modeling may mask one or more spans of the input signal sequence (e.g., up to approximately 10 seconds in duration), and the encoder may be trained to predict or reconstruct the masked content using contextual information from unmasked segments, thereby learning temporal dependencies and morphology-related features. As another example, cross-modal prediction may be used in which the encoder is trained to predict features of a first modality from one or more other modalities (and optionally vice versa), thereby learning physiologic relationships among electrical cardiac activity, mechanical cardiac activity, and vascular volume changes. As a further example, contrastive alignment objectives may be applied to encourage embeddings corresponding to temporally synchronized segments (or otherwise physiologically similar segments) to be closer in an embedding space than embeddings corresponding to temporally distant segments or physiologically dissimilar segments. The particular loss functions, masking strategies, and training formulations may vary across embodiments, and any of the foregoing objectives may be used alone or in combination.
[0044] The encoding process may incorporate cardiovascular and respiratory domain knowledge into the transformer encoder architecture design through specialized attention mechanisms that focus on cardiac cycle phases, respiratory modulation patterns, and autonomic nervous system influences on physiological signals. The model architecture may include dedicated pathways for processing heart rate variability features, circadian rhythm patterns, and stress-related physiological signatures that reflect clinical understanding of cardiovascular and respiratory physiology.
[0045] An illustrative example of a windowed embedding generation process using multimodal physiological data acquired from sensors is now discussed. A multimodal physiological record may be acquired over a monitoring period using sensors configured to capture BCG signals from piezoelectric sensors embedded in bed frames, photoplethysmogram signals from wearable devices, tri-axial accelerometer measurements, and skin temperature data at sampling frequencies suitable for physiological monitoring applications. The acquired signals may be partitioned into overlapping analysis windows with configurable duration and stride parameters to enable comprehensive processing while maintaining temporal context for physiological pattern recognition. For each analysis window, the preprocessed signals from the BCG, photoplethysmogram, accelerometer, and temperature modalities may be organized into a structured input representation suitable for processing by the transformer encoder. The preprocessing module applies the signal conditioning operations described above to clean and normalize the multimodal data, after which the encoder directly processes the preprocessed signal samples without requiring explicit feature extraction, as the transformer architecture learns relevant representations directly from the input data through its attention mechanisms. Following per-dimension normalization to ensure consistent scaling across different feature types, the encoder may transform each window's multimodal feature vector into a shared embedding representation with reduced dimensionality suitable for downstream computational processes. The resulting embeddings may provide compact representations that preserve physiological information while enabling efficient processing by task-specific inference modules for clinical monitoring applications.
[0046] Referring again to FIG. 1, the method 100 advances to step 110, where the shared embedding is stored in a machine-readable store. The storage process may implement a circular embedding store with integrity verification mechanisms. The circular embedding store may implement a ring buffer data structure with fixed memory allocation to prevent unbounded memory growth during continuous monitoring applications, where older embeddings are automatically overwritten when storage capacity is reached. Integrity verification mechanisms may provide data integrity verification for stored embeddings, enabling detection of memory corruption or transmission errors that could compromise downstream analysis accuracy.
[0047] The method 100 concludes with step 112, where the shared embedding is exposed for consumption by one or more downstream computational processes. The downstream processes may include task heads that generate clinical outputs such as cardiac arrhythmia detection, heart failure risk assessment, sleep apnea severity scoring, or cardiovascular health monitoring from the shared embeddings. The task heads may comprise neural network modules that process the shared embeddings to generate task-specific predictions. The shared embeddings provide rich, pre-learned physiological representations that significantly reduce the amount of labeled data required to train each task head, as the complex feature extraction has already been performed by the self-supervised encoder. Each task head can be trained with relatively small amounts of task-specific labeled data because it only needs to learn the mapping from the high-quality shared embedding space to the desired clinical output, rather than learning to extract features directly from raw physiological signals. This approach enables rapid development of new clinical monitoring applications and improved performance on tasks with limited labeled training data, where cardiac arrhythmia detection heads may implement neural networks trained on embedding representations to identify abnormal heart rhythm patterns, heart failure monitoring heads may utilize embedding-based classifiers to assess cardiac dysfunction severity, sleep apnea detection heads may employ embedding-based models to identify respiratory disturbances during sleep, and cardiovascular risk assessment heads may implement embedding-based prediction models focused on identifying early indicators of cardiac disease progression.
[0048] In various embodiments, downstream computational processes consume the shared embeddings to compute control signals for clinical decision support systems. The control signals may provide recommendations for medication adjustments, lifestyle interventions, or clinical follow-up scheduling based on the learned physiological state. Clinical decision support systems may utilize the embeddings to generate alerts for healthcare providers when physiological patterns indicate potential health risks or deterioration in patient condition.
[0049] For cardiac monitoring applications, embeddings indicative of arrhythmia patterns may trigger automated alerts to healthcare providers with severity scoring and recommended intervention protocols. The system may generate risk stratification reports based on embedding analysis that help clinicians prioritize patient care and allocate resources effectively. Clinical documentation systems may utilize embeddings to automatically generate physiological summaries and trend reports for electronic health records.
[0050] During embeddings consistent with heart failure progression, the system may generate recommendations for medication titration, fluid management protocols, and scheduling of cardiology consultations. The embeddings may be used to track treatment response and adjust therapeutic interventions based on physiological changes detected through continuous monitoring.
[0051] When embeddings indicate respiratory disturbances, downstream systems may initiate protocols for sleep study referrals, continuous positive airway pressure (CPAP) therapy adjustments, or pulmonology consultations. The embeddings may be used to monitor treatment efficacy and guide therapeutic decision-making for respiratory conditions.
[0052] Embeddings also enable population health analytics where aggregated embedding patterns across patient cohorts may identify epidemiological trends, treatment response patterns, and risk factors for cardiovascular disease. This constitutes a practical improvement to clinical care through embedding-based physiological analysis and evidence-based decision support.
[0053] In various embodiments, the shared embedding is transformed by a downstream computational process into clinical insights that support healthcare provider decision-making. The clinical insights may include risk scores, trend analyses, comparative assessments, and treatment recommendations transmitted through secure healthcare information systems to clinical interfaces. The downstream processes may include, without limitation, cardiac risk stratification systems, heart failure monitoring platforms, arrhythmia detection algorithms, sleep disorder assessment tools, and cardiovascular health tracking applications, whereby processing of the embeddings produces clinical recommendations and alerts that support evidence-based patient care.
[0054] The system may integrate with a collection of sensors including ECG, BCG, and SpO2 sensors, where the generated embeddings are stored in a vectorized database enabling similarity querying for patient comparison and trend analysis. The embeddings may feed into detection algorithms configured to detect specific health conditions including arrhythmias through cardiac rhythm pattern recognition in embedding space, heart failure progression through embedding-based trend analysis, and respiratory disorders through breathing pattern detection using embedding representations. The detection algorithms may utilize the shared embeddings as input features, benefiting from the rich physiological representations learned through self-supervised training on large unlabeled datasets. The embeddings provide these algorithms with robust, noise-reduced features that capture cross-modal physiological relationships, enabling more accurate detection performance compared to conventional algorithms that process raw sensor signals directly. The shared embedding approach allows these health condition detection algorithms to leverage the same high-quality physiological representations, reducing the computational overhead and improving generalization across different patient populations and sensor configurations while providing comprehensive physiological monitoring capabilities from the shared embedding representations.
[0055] Referring to FIG. 2, a method 200 for self-supervised multimodal biosignal representation learning incorporates error handling capabilities and retry mechanisms to ensure robust operation under varying signal quality conditions. The method 200 begins with step 202, where sensors are configured to acquire synchronized multimodal biosignals. The sensor configuration process may involve establishing communication protocols between various sensor types including bed-based BCG sensors, wearable PPG devices, chest-mounted ECG electrodes, accelerometer sensors, temperature sensors, impedance sensors, and pulse oximetry sensors, where each sensor type may require specific initialization parameters including sampling frequency settings, gain adjustments, and filtering configurations. The configuration process may include sensor calibration procedures to account for individual device characteristics, baseline drift compensation, and signal range optimization.
[0056] The method 200 proceeds to step 204, which involves initializing a synchronization module for hardware timestamping and drift compensation. The synchronization module initialization may establish timing reference sources using precision time protocol (PTP) servers or connection interval synchronization mechanisms, where timing accuracy requirements may be configured based on the specific physiological monitoring application. The initialization process may include clock skew measurement procedures that characterize timing differences between distributed sensor nodes, enabling subsequent drift compensation algorithms to maintain temporal alignment accuracy throughout extended monitoring sessions. The synchronization module may implement adaptive timing correction algorithms that continuously monitor and adjust for clock drift variations caused by temperature changes, aging effects, and power supply fluctuations in wireless sensor devices.
[0057] With continued reference to FIG. 2, the method 200 moves to step 206, where a preprocessing module is activated for modality-specific filtering and denoising. The preprocessing module may include a movement detection module that uses techniques to detect signal saturation due to movements and a processability module that assesses signal quality. The movement detection module may analyze accelerometer data streams to identify periods of patient motion that may contaminate physiological signal measurements, implementing threshold-based detection algorithms that trigger artifact suppression mechanisms when acceleration magnitudes exceed a configurable threshold. The processability module may employ quality assessment algorithms trained on signal quality metrics including signal-to-noise ratio, baseline stability, and morphological consistency to generate quality scores for each signal modality.
[0058] The preprocessing module activation may configure denoising parameters, for example utilizing wavelet-based denoising with configurable wavelet types, where each wavelet type provides different time-frequency localization characteristics suitable for specific physiological signal morphologies. Adaptive thresholding may apply different threshold values to individual signal components based on statistics computed over configurable analysis windows, enabling selective noise reduction while preserving physiological signal components across different frequency bands. The modality-specific filtering may further comprise accelerometer-gated motion artifact rejection configured to suppress noise during detected movement periods exceeding a configurable acceleration magnitude threshold, where motion detection algorithms provide contextual information for adaptive filtering parameter adjustment.
[0059] The method 200 continues to step 208, where an encoder is executed on a processor with windowed segments. The processor execution may utilize specialized neural processing units (NPUs) or graphics processing units (GPUs) optimized for encoder architecture computations, where memory allocation strategies may be configured to accommodate windowed segment processing with configurable duration and hop intervals. The encoder execution may implement quantized inference using reduced-precision arithmetic operations with appropriate scale and zero-point parameters to reduce memory bandwidth requirements and enable deployment on resource-constrained computing platforms. The windowed segment processing may utilize circular buffer data structures to maintain temporal context while minimizing memory allocation overhead during monitoring applications.
[0060] As further shown in FIG. 2, the method 200 reaches a decision point at step 210, which determines whether embeddings are successfully generated. The success determination may evaluate multiple criteria including embedding vector completeness, numerical stability checks for avoiding infinite or not-a-number values, and embedding quality metrics based on reconstruction error thresholds or consistency measures across temporal windows. The decision logic may implement timeout mechanisms to prevent indefinite processing delays when input signals contain corrupted data or when computational resources are insufficient for processing requirements. The embedding generation success evaluation may include out-of-distribution detection using distance computations in the embedding space to identify physiological signals that deviate significantly from training data distributions.
[0061] The out-of-distribution detection may implement statistical analysis of embedding vectors using distance computation with statistical parameters of the training embedding distribution, where the statistical parameters capture the relationships between different embedding dimensions learned during self-supervised training. The learned distribution boundaries may be established during training by computing statistical parameters from embedding representations, with threshold values based on percentile statistics computed for training embeddings. The output suppression mechanism may implement graduated response strategies where mildly out-of-distribution signals trigger confidence score reductions while severely out-of-distribution signals result in complete output suppression and user notifications about potential measurement issues, functioning as a reliability-preserving control path that may suppress or defer outputs, initiate resynchronization, increase sampling robustness, or request additional measurement context when deviation is detected.
[0062] When embeddings are successfully generated, the method 200 proceeds to step 212, where the embeddings are stored in a circular embedding store with integrity verification and time-to-live (TTL) coalescing. The storage process may implement atomic write operations to ensure data consistency during concurrent access scenarios, where multiple task heads may simultaneously read embedding data while new embeddings are being written to the circular buffer. The integrity verification mechanisms may utilize error detection codes to verify data integrity during storage and retrieval operations, enabling automatic detection of memory corruption events that could compromise downstream analysis accuracy. The TTL coalescing may aggregate embedding vectors over configurable time intervals before storage or network transmission, reducing storage overhead while maintaining temporal resolution sufficient for clinical monitoring applications.
[0063] The circular embedding store may support time-keyed similarity search. In some embodiments, the system indexes embeddings by acquisition time and device identifiers and supports nearest-neighbor retrieval to surface physiologically similar epochs. Such retrieval may facilitate trend analysis, baseline comparison, cohorting for quality review, and targeted re-evaluation when model versions are updated.
[0064] The circular embedding store described herein further provides a computer-implemented improvement by enabling atomic writes and integrity verification under concurrent access, thereby reducing corruption risk and bandwidth for upload without sacrificing clinically relevant temporal resolution. TTL-based coalescing may be adaptively configured according to network conditions or policy without altering analytical fidelity.
[0065] When embeddings are not successfully generated, the method 200 moves to step 214, where embedding generation is retried with adjusted parameters. The parameter adjustment process may modify preprocessing filter settings, reduce input signal window lengths, adjust quantization precision levels, or implement alternative processing mechanisms with reduced computational complexity. The retry mechanism may implement backoff strategies to prevent excessive computational resource consumption during repeated failure conditions, where retry intervals may increase progressively based on failure frequency. The adjusted parameters may include fallback processing modes that utilize simplified signal processing algorithms or reduced-complexity neural network architectures when full-featured processing fails due to resource constraints or signal quality limitations.
[0066] Both step 212 and step 214 subsequently lead to step 216, where clinical outputs are generated using task heads. The task head execution may implement conditional processing logic that adapts output generation strategies based on embedding quality metrics and available computational resources, where high-quality embeddings may enable full-featured multi-task inference while lower-quality embeddings may trigger simplified processing modes focused on the most reliable physiological measurements. The clinical output generation may include confidence scoring mechanisms that quantify prediction reliability based on embedding consistency, signal quality assessments, and historical performance metrics, enabling downstream clinical decision support systems to appropriately weight automated physiological monitoring results.
[0067] Referring to FIG. 3, a method 300 for self-supervised multimodal biosignal representation learning incorporates conditional training logic and computer-readable medium implementation to provide flexible deployment options for both training and inference scenarios. The method 300 begins with step 302, where instructions are executed to acquire synchronized multimodal biosignals including at least two of ballistocardiogram (BCG), electrocardiogram (ECG), and photoplethysmogram (PPG) signals from one or more sensors. The instruction execution may be performed by a processor accessing a non-transitory computer-readable medium storing program code that configures sensor interfaces, establishes communication protocols, and manages data acquisition buffers for physiological signal collection. The synchronized multimodal biosignals may be acquired through coordinated sampling operations that maintain temporal relationships between different sensor modalities, where each signal type provides complementary physiological information about cardiovascular and respiratory system activity.
[0068] The method 300 proceeds to step 304, where hardware timestamping and drift compensation are applied via a processor to generate synchronized signals with reduced temporal misalignment. The processor may execute timestamping algorithms that implement precision time protocol (PTP) or connection interval tagging to achieve temporal alignment accuracy between the multimodal biosignals. The hardware timestamping operations may utilize dedicated timing hardware including crystal oscillators, phase-locked loops, and timestamp counters to provide timing precision across distributed sensor networks. The drift compensation algorithms may monitor clock skew accumulation over extended monitoring periods, applying correction factors based on models that account for temperature-dependent frequency variations and aging effects in sensor timing circuits.
[0069] With continued reference to FIG. 3, the method 300 advances to step 306, where modality-specific filtering and denoising are performed on the synchronized signals using adaptive thresholding to generate preprocessed signals with improved signal-to-noise ratio. The denoising may utilize wavelet-based techniques with configurable wavelet types, where each wavelet type provides different time-frequency localization characteristics optimized for specific physiological signal morphologies and noise characteristics. The adaptive thresholding may apply different threshold values to individual signal components based on statistics computed over configurable analysis windows, enabling selective noise reduction that preserves physiological signal components while attenuating artifacts and measurement noise across different frequency bands.
[0070] The modality-specific filtering may further comprise accelerometer-gated motion artifact rejection configured to suppress noise during detected movement periods exceeding a configurable acceleration magnitude threshold, where accelerometer data provides contextual information about patient movement that may contaminate physiological measurements. The filtering operations may apply band-pass filtering with modality-appropriate frequency ranges to preserve clinically relevant signal components while removing baseline wander and high-frequency noise.
[0071] The method 300 continues to step 308, where quantized shared embeddings are generated from the preprocessed signals using a pre-trained encoder with windowed segments and attention mechanisms. The pre-trained encoder may have been trained on large-scale datasets containing a plurality of signal segments, providing baseline physiological pattern recognition capabilities across diverse sensor configurations. The encoder may process temporal sequences to capture physiological patterns across different time scales and modalities. The windowed segments may utilize configurable duration with configurable hop intervals to provide sufficient temporal context for physiological pattern recognition. The attention mechanism may enable the model to focus on relevant temporal relationships and cross-modal correlations, while grouped operations may partition processing into independent groups to optimize computational efficiency and enable parallel computation.
[0072] As further shown in FIG. 3, the method 300 reaches decision step 310, which determines whether sufficient training data is available. The training data availability determination may evaluate multiple criteria including the quantity of unlabeled multimodal biosignal recordings accessible for self-supervised learning, the diversity of patient populations represented in available datasets, and the coverage of different physiological conditions and sensor configurations within the training corpus. The decision logic may assess data volume metrics, demographic representation, and signal quality distributions to determine whether adequate training resources exist to support effective self-supervised learning or whether a reduced training regime should be employed.
[0073] Embedding validation may be performed through clustering algorithms applied to the learned embedding representations, where clustering techniques may group similar physiological patterns in the embedding space generated by the encoder. Visualization of signals within each cluster may verify that embeddings capture meaningful physiological similarities, where signals with similar cardiac morphology, respiratory patterns, or autonomic activity should be grouped together in the embedding space. Linear probing performance assessment may evaluate the quality of learned embeddings by training simple linear classifiers on frozen embedding representations for various downstream tasks including cardiac arrhythmia detection, heart failure monitoring, and sleep disorder assessment, providing a standardized evaluation metric that isolates the quality of learned representations from the complexity of downstream task-specific architectures.
[0074] When sufficient training data is available (Yes branch), the method 300 proceeds to step 312, where the encoder is trained using self-supervised objectives including masked segment modeling, cross-modal prediction, and contrastive alignment to learn generalized physiological representations. The masked segment modeling comprises training the encoder by using training data consisting of synchronized BCG, ECG, and PPG signal recordings from diverse patient populations, similar in type to the signals processed during inference but used specifically for self-supervised learning without requiring clinical labels or annotations. During masked segment modeling, portions of these training signals are masked and the encoder learns to reconstruct them from surrounding context.
[0075] For cross-modal prediction, the encoder learns to predict one signal modality from others using the same unlabeled training data. Contrastive alignment uses temporally synchronized segments from the training dataset as positive pairs while treating temporally distant segments as negative pairs. The training process enables the encoder to learn robust physiological representations from the inherent structure and relationships within the multimodal biosignal data itself, without requiring separate labeled datasets for supervised learning. The masked segment modeling may apply variable mask spans to input signal sequences, where the encoder learns to reconstruct masked portions using contextual information from unmasked segments, enabling the model to capture temporal dependencies and physiological signal morphology patterns. The cross-modal prediction may implement cycle-consistent loss functions as an example training objective for bidirectional signal prediction between modalities, where the model learns to predict features of one modality from other modalities and vice versa, capturing physiological relationships between electrical cardiac activity, mechanical cardiac activity, and vascular volume changes. The contrastive alignment may utilize contrastive loss functions as another example training objective with temperature scaling parameters to align embeddings from temporally synchronized signal segments while separating embeddings from temporally distant or physiologically dissimilar segments.
[0076] The encoder training using masked segment modeling, cross-modal prediction, and contrastive alignment may incorporate data augmentation techniques that create overlapping segments with configurable overlap between consecutive windows, enabling the encoder to learn invariant representations across different temporal positions within physiological recordings. Time shifting augmentation may preserve the relative timing relationships between BCG, ECG, and PPG signals while providing diverse temporal contexts for self-supervised learning, where the same physiological events may be observed at different positions within analysis windows to improve temporal generalization capabilities. Class imbalance handling may utilize minority class oversampling techniques that replicate underrepresented physiological patterns during training and weighted loss functions that apply weighting mechanisms to increase the contribution of rare physiological patterns to the overall training objective, preventing the encoder from being biased toward common physiological patterns while ensuring adequate learning of clinically relevant but infrequent physiological abnormalities.
[0077] When sufficient training data is not available, the method 300 moves to step 314, where the encoder is trained using a reduced training regime. The reduced training regime may employ transfer learning from pre-trained models, utilize smaller batch sizes, implement fewer training epochs, or apply simplified self-supervised objectives that require less training data to achieve acceptable embedding quality. The reduced training approach may prioritize computational efficiency and rapid deployment while accepting potentially reduced generalization performance compared to full-scale training with comprehensive datasets.
[0078] An illustrative example of a transformer encoder configuration is now described. The encoder may utilize a transformer-based architecture with multihead attention and a one-dimensional convolutional neural network as a feature extractor that processes raw physiological signals before feeding them into transformer layers. The encoder may implement attention mechanisms with limited temporal context and stride-based attention patterns, with grouped query-key-value operations for memory locality, and may generate embeddings with dimensionality in the range of 128 to 256 dimensions depending on application requirements. The windowed segment processing may utilize 8-second windows with 1-second hop intervals to provide sufficient temporal context for physiological pattern recognition while enabling responsive processing. The training configuration may utilize a learning rate of 1.5e-3, a batch size of 4096 signal segments, and 200 training epochs to achieve convergence on large-scale physiological datasets. A learning rate scheduler may be used for stable learning, along with Adam optimization for adaptive learning rate adjustments and mixed precision training to reduce memory consumption while preserving numerical stability. The masked segment modeling may apply random mask spans of 20 to 200 milliseconds duration, where masked portions are reconstructed by a transformer decoder head using a combination of time-domain loss and frequency-domain short-time Fourier transform loss to capture both temporal and spectral characteristics of physiological signals. Cross-modal prediction may predict ECG morphology from PPG and accelerometer context using cycle-consistent loss functions that penalize drift between predicted and actual signal characteristics. Contrastive alignment may utilize InfoNCE loss across same-event different-modality positive pairs versus temporally distant negative pairs with temperature-scaled softmax normalization and hard negative mining from similar activity states to improve discrimination between physiologically similar but temporally distinct signal segments. Data augmentations may include time-warp of up to 5 percent, amplitude jitter of up to 3 decibels, and cutout of up to 100 milliseconds duration, with augmentations forbidden when accelerometer data suggests arrhythmia-like patterns to prevent the introduction of physiologically implausible training examples.
[0079] Both step 312 and step 314 converge at step 316, where the shared embeddings are stored and task heads are executed. The storage operations may implement atomic write mechanisms to ensure data consistency during concurrent access scenarios, where multiple task heads may simultaneously read embedding data while new embeddings are being generated and stored. The system may generate fresh embeddings for all instances including older datasets, ensuring backward compatibility without version control issues, where embedding generation algorithms remain consistent across different data collection periods and sensor hardware revisions. The task head outputs may include confidence scoring mechanisms that quantify prediction reliability based on embedding quality metrics, signal characteristics, and historical performance data, enabling clinical decision support systems to appropriately weight automated physiological monitoring results based on measurement conditions and patient-specific factors.
[0080] The encoder architecture implements attention mechanisms specifically adapted for physiological signal processing, where the encoder processes temporal sequences of biosignal data and generates embedding representations during training and inference. The encoder may utilize a convolutional neural network as a feature extractor that processes raw physiological signals before feeding them into subsequent layers, where the convolutional network may apply filters to capture local temporal patterns in BCG, ECG, and PPG signals. The attention mechanism may partition the embedding space into multiple attention heads, where each head may focus on different aspects of physiological signal relationships such as cardiac cycle timing, respiratory modulation patterns, and cross-modal correlations between different sensor measurements.
[0081] The masked segment modeling applies variable mask spans using span masking techniques where continuous tokens over a time period are masked from input data and other tokens predict them using contextual information. The span masking approach may differ from traditional token-level masking used in natural language processing by masking contiguous temporal segments that correspond to physiological events such as cardiac cycles, respiratory phases, or movement artifacts. The variable mask span duration may be selected randomly during training to encourage the model to learn representations at multiple temporal scales, where shorter mask spans may focus on fine-grained signal morphology while longer mask spans may capture broader physiological patterns and inter-signal relationships.
[0082] The cross-modal prediction implements cycle-consistent loss functions as an example training objective for bidirectional signal prediction between modalities, enabling the model to learn shared representations that capture physiological relationships between different sensor measurements. The cycle-consistent loss may enforce that predictions from one modality to another and back to the original modality should reconstruct the original signal, where signals of one modality may be predicted from other modality inputs and subsequently used to reconstruct the original signals. The bidirectional prediction capability may enable the model to learn complementary information across sensor modalities, where mechanical cardiac activity measured by BCG sensors may be used to predict electrical cardiac activity captured by ECG sensors, and optical blood volume changes detected by PPG sensors may be used to predict both mechanical and electrical cardiac characteristics.
[0083] The contrastive alignment utilizes contrastive loss functions as another example training objective with temperature scaling parameters to align embeddings from temporally synchronized signal segments while separating embeddings from temporally distant or physiologically dissimilar segments. The contrastive loss function may implement a softmax-based contrastive objective that maximizes similarity between positive pairs of synchronized signal segments while minimizing similarity to negative pairs sampled from different time periods or physiological states. The temperature scaling parameter may control the sharpness of the similarity distribution, where lower temperature values may increase the discrimination between positive and negative pairs while higher temperature values may provide smoother similarity gradients during training optimization.
[0084] The training configuration may utilize appropriate learning rates with suitable batch sizes and training epochs to achieve convergence on large-scale physiological datasets. The learning rate scheduler may implement annealing or decay strategies for stable learning, where the learning rate may be gradually reduced during training to enable fine-grained parameter optimization in later training stages. Optimization algorithms may provide adaptive learning rate adjustments for individual model parameters based on gradient statistics, enabling efficient convergence across the diverse parameter spaces of attention mechanisms and convolutional feature extractors. Mixed precision training may utilize reduced-precision arithmetic for forward and backward propagation while maintaining higher precision for parameter updates, reducing memory consumption and enabling larger batch sizes on hardware accelerators while preserving numerical stability.
[0085] Regularization techniques may include weight decay applied to attention weights and convolutional filter parameters to prevent overfitting during self-supervised pre-training on large physiological datasets. Dropout layers may be applied to attention outputs and feed-forward network activations to encourage robust feature learning and reduce dependency on specific neural pathway activations. Data augmentation may be performed by time shifting signals and creating multiple overlapping segments for the same signal, where temporal shifts may generate additional training examples while preserving physiological signal integrity and temporal relationships between different sensor modalities.
[0086] An illustrative example of training results and performance outcomes is now described. The transformer encoder may be trained using approximately 500,000 to 650,000 signal segments of 15 seconds each from diverse patient populations, with a minimum of approximately 500,000 signal segments recommended for effective self-supervised learning of generalized physiological representations. Target performance metrics may include processing latency of 150 milliseconds or less per analysis window on mid-range mobile phone CPU or NPU hardware, power consumption of 30 milliwatts or less on average on wearable system-on-chip devices during inference, and storage requirements of 20 kilobytes per minute or less for compressed embeddings transmitted to cloud storage. Clinical performance targets may include respiratory rate estimation with mean absolute error of 1.5 breaths per minute or less, sleep staging with F1 score of 0.78 or greater for 30-second epoch classification, and anomaly detection with area under receiver operating characteristic curve of 0.90 or greater for identifying clinically significant physiological deviations. Embedding quality may be assessed by performing clustering algorithms on learned embedding representations and visualizing signals within each cluster to verify that physiologically similar patterns are grouped together, checking similarity of embeddings that have the same ground truth value for downstream tasks, and linear probing to evaluate performance of frozen embeddings in downstream tasks using simple linear classifiers that isolate representation quality from task-specific architecture complexity. The embeddings may be validated using unlabeled BCG data to learn self-supervised representations followed by fine-tuning on downstream respiratory rate estimation tasks to demonstrate transfer learning capabilities and clinical utility of the learned physiological representations.
[0087] Referring to FIG. 4, a method 400 for comprehensive monitoring workflow with quality assessment provides a complete end-to-end implementation of the physiological embedding generation system described in methods 100-300, incorporating quality-controlled processing pipelines that adapt to varying signal conditions through conditional preprocessing enhancement and sensor integration protocols. Method 400 demonstrates a practical deployment scenario that combines the core embedding generation steps from method 100, the error handling mechanisms from method 200, and the conditional training logic from method 300 into a unified monitoring workflow suitable for clinical applications. The method 400 begins with step 402, where a monitoring session is initiated through a mobile application interface. The mobile application interface may provide user controls for configuring monitoring parameters including session duration, sensor selection preferences, and clinical monitoring objectives such as cardiac monitoring, respiratory assessment, or sleep analysis. The application interface may implement authentication mechanisms to ensure patient privacy and data security, while providing status displays showing sensor connectivity, signal quality indicators, and processing status information. The monitoring session initiation may include patient identification procedures, baseline physiological parameter collection, and calibration routines that establish individual-specific reference values for subsequent analysis algorithms that will consume the generated embeddings.
[0088] The method 400 proceeds to step 404, which involves detecting and pairing bed-based BCG, wrist PPG, and chest ECG sensors. The sensor detection process may utilize wireless discovery protocols to identify available physiological monitoring devices within communication range, where each sensor type may broadcast unique service identifiers that enable automatic recognition and classification. The pairing process may implement secure authentication mechanisms including cryptographic key exchange protocols to prevent unauthorized access to physiological data streams, while establishing reliable communication channels that maintain connectivity throughout extended monitoring sessions. The bed-based BCG sensors may be configured for mechanical cardiac activity detection through various sensing technologies, wrist PPG sensors may be optimized for optical blood volume monitoring with ambient light rejection capabilities, and chest ECG sensors may provide high-fidelity electrical cardiac activity measurements with motion artifact suppression features.
[0089] With continued reference to FIG. 4, the method 400 moves to step 406, where temporal synchronization is established across all sensor modalities. The temporal synchronization process may implement precision time protocol (PTP) or connection interval tagging mechanisms to achieve temporal alignment accuracy between the multimodal biosignals acquired from the bed-based BCG, wrist PPG, and chest ECG sensors. The synchronization establishment may include clock skew measurement procedures that characterize timing differences between distributed sensor nodes, enabling subsequent drift compensation algorithms to maintain temporal coherence throughout the monitoring session. The synchronization process may implement adaptive timing correction algorithms that continuously monitor and adjust for clock drift variations caused by temperature changes, battery voltage fluctuations, and wireless communication latency variations in the distributed sensor network.
[0090] The method 400 continues to step 408, where preprocessing is applied with modality-specific filtering and artifact rejection. The preprocessing operations may implement band-pass filtering with modality-appropriate frequency ranges to preserve clinically relevant signal morphology while attenuating baseline wander and high-frequency noise. The modality-specific filtering may further comprise accelerometer-gated motion artifact rejection configured to suppress noise during detected movement periods exceeding a configurable acceleration magnitude threshold, where accelerometer data streams provide contextual information about patient movement that may contaminate physiological signal measurements from any of the sensor modalities.
[0091] The accelerometer-gated motion artifact rejection may implement threshold-based detection algorithms that monitor acceleration measurements to identify periods of patient motion, body position changes, or external disturbances that may affect signal quality. When acceleration magnitudes exceed a configurable threshold, the artifact rejection system may apply adaptive filtering parameters, increase noise suppression levels, or temporarily suspend certain signal processing operations until motion subsides. The motion detection algorithms may utilize sliding window analysis with configurable time constants to balance responsiveness to transient movements while avoiding excessive sensitivity to minor position adjustments or breathing-related motion.
[0092] As further shown in FIG. 4, the method 400 reaches decision point 410, which determines whether signal quality is acceptable across all modalities. The signal quality assessment may evaluate multiple criteria including signal-to-noise ratio measurements, baseline stability metrics, morphological consistency indicators, and correlation measures between synchronized signal modalities. The quality determination algorithms may implement threshold-based decision logic that compares computed quality metrics against predetermined acceptance criteria, where signal quality may be assessed based on detectability and consistency metrics appropriate for each modality. The decision point may incorporate temporal averaging mechanisms that prevent rapid switching between processing modes due to transient signal quality variations, implementing hysteresis logic that requires sustained quality improvements or degradations before triggering processing mode changes.
[0093] When signal quality is acceptable across all modalities, the method 400 proceeds to step 412, where embeddings are generated using an encoder. The embedding generation process may utilize the preprocessed signals from all sensor modalities to create quantized shared embeddings with appropriate dimensionality using quantization with suitable scale and zero-point parameters. The encoder may process windowed segments with configurable duration and hop intervals to provide sufficient temporal context for physiological pattern recognition. The encoder may implement attention mechanisms and grouped operations to optimize computational efficiency while preserving the ability to capture temporal dependencies and cross-modal relationships between BCG, ECG, and PPG signal characteristics.
[0094] Step 412 leads to step 414, where multiple task heads are executed for cardiac arrhythmia detection, heart failure monitoring, and sleep disorder assessment. The cardiac arrhythmia detection task head may implement neural networks trained on embedding representations to identify abnormal heart rhythm patterns including atrial fibrillation, ventricular tachycardia, and premature ventricular contractions. The heart failure monitoring task head may employ embedding-based classifiers to assess cardiac dysfunction severity and track disease progression over time. The sleep disorder assessment task head may utilize embedding-based models to identify sleep apnea events, sleep stage transitions, and other sleep-related physiological disturbances based on the shared embedding representations generated from the synchronized multimodal biosignals.
[0095] The health condition detection algorithms may implement specialized classifiers trained on embedding representations to identify physiological signatures associated with specific medical conditions, where cardiovascular risk assessment may analyze embedding patterns that correlate with increased risk of cardiac events, and respiratory disorder detection may identify embedding signatures associated with sleep apnea, chronic obstructive pulmonary disease, or other respiratory conditions. For cardiac monitoring applications, the system may provide clinical alerts and risk stratification reports to healthcare providers, and for respiratory monitoring, the system may generate treatment recommendations and therapy adjustment suggestions. Priority routing may integrate clinical decision support workflows, where high-risk anomaly scores initiate secure communications to healthcare providers while generating clinical documentation and trend reports for electronic health records. Additionally, smart bed applications may utilize embeddings to adjust bed positioning, ambient lighting, and environmental controls to optimize sleep quality and comfort, though these applications represent secondary use cases compared to the primary clinical monitoring objectives.
[0096] Referring to FIG. 5, a multimodal biosignal processing system 500 provides integrated hardware and software components for self-supervised multimodal biosignal representation learning with processing capabilities. The multimodal biosignal processing system 500 may implement distributed processing architectures that enable deployment across edge computing platforms, mobile devices, and cloud-based infrastructure while maintaining processing requirements for physiological monitoring applications. The system architecture may incorporate modular design principles that enable independent scaling and optimization of individual processing components based on computational resource availability and clinical monitoring requirements.
[0097] The multimodal biosignal processing system 500 may further comprise a model update orchestrator configured to manage deployment of updated encoder versions while maintaining continuous operation.
[0098] The model update orchestrator may implement shadow evaluation that collects performance metrics including prediction accuracy on validation datasets, processing latency measurements, memory consumption profiles, and embedding quality assessments for both production and shadow encoder versions. The evaluation metrics may be aggregated over configurable time periods to ensure statistical significance of performance comparisons while accounting for temporal variations in physiological signal characteristics and patient populations. The rollback functionality may provide rapid reversion to previous encoder versions when updated models exhibit degraded performance, implementing automated monitoring of key performance indicators where threshold violations trigger automatic rollback procedures that restore previous model weights, configuration parameters, and preprocessing settings. The continuous operation maintenance may ensure that physiological monitoring services remain available throughout model update procedures through load balancing mechanisms that distribute inference requests across multiple model instances and graceful degradation strategies that activate reduced functionality modes if primary model instances become unavailable.
[0099] A sensor acquisition module 502 comprises multiple sensor interfaces configured to acquire synchronized multimodal biosignals for physiological monitoring applications. The sensor acquisition module 502 may implement standardized communication protocols including wireless and wired connections to support diverse sensor hardware configurations and deployment scenarios. The sensor acquisition module 502 may include buffer management systems that maintain temporal coherence between different sensor data streams while accommodating varying sampling rates and communication latencies across heterogeneous sensor networks.
[0100] The sensor acquisition module 502 includes a BCG sensor interface 504 configured to acquire ballistocardiogram signals from bed-based sensors measuring cardiac mechanical activity through body movement detection. The BCG sensor interface 504 may implement signal conditioning circuits that amplify low-amplitude mechanical signals while filtering environmental vibrations and motion artifacts that may interfere with cardiac activity measurements. The BCG sensor interface 504 may support various sensing technologies including piezoelectric transducers, pneumatic sensors, optical sensors, accelerometer-based sensors, and load cell sensors, with configurable sampling frequencies and gain settings and baseline correction algorithms that adapt to individual patient characteristics and bed sensor configurations.
[0101] An ECG sensor interface 506 may be configured to acquire electrocardiogram signals from chest-mounted electrodes capturing electrical cardiac activity with high temporal resolution. The ECG sensor interface 506 may implement differential amplification circuits with common-mode rejection to minimize interference from power line noise, electromagnetic fields, and muscle activity. The ECG sensor interface 506 may support configurable sampling frequencies with programmable gain amplifiers and circuits that maintain signal quality during patient movement and electrode impedance variations.
[0102] A PPG sensor interface 508 may be configured to acquire photoplethysmogram signals from wearable devices monitoring blood volume changes through optical measurements. The PPG sensor interface 508 may implement ambient light rejection algorithms that compensate for environmental illumination variations while maintaining sensitivity to pulsatile blood volume changes. The PPG sensor interface 508 may support configurable sampling frequencies with automatic gain control mechanisms that adapt to skin pigmentation, contact pressure, and motion artifacts commonly encountered in wearable sensor applications.
[0103] The BCG sensor interface 504, ECG sensor interface 506, and PPG sensor interface 508 may be connected to a synchronization module 510 that receives multimodal biosignal data from the different sensor interfaces and applies temporal alignment algorithms. The synchronization module 510 may implement buffer management systems that accommodate different sensor sampling rates and communication latencies while maintaining temporal relationships between physiological measurements acquired from distributed sensor nodes. The synchronization module 510 may include data integrity verification mechanisms that detect and correct transmission errors, packet loss, and timing inconsistencies that may occur during wireless sensor communication.
[0104] The synchronization module 510 includes hardware timestamping 512 configured to implement precision time protocol (PTP) or connection interval tagging to achieve temporal alignment accuracy between the multimodal biosignals. The hardware timestamping 512 may utilize dedicated timing hardware including crystal oscillators, phase-locked loops, and timestamp counters that provide timing precision across distributed sensor networks. The hardware timestamping 512 may implement network-based clock distribution mechanisms for PTP synchronization or connection event timestamps for wireless sensor coordination, enabling coherent multimodal analysis of physiological signals acquired from sensors with different hardware characteristics and communication protocols.
[0105] In certain embodiments, cross-modal fusion is performed after establishing a common temporal reference across modalities. The system may implement one or more of: resampling to a shared reference frame; attention-based alignment with learned temporal offsets; differentiable time-warping to compensate for nonuniform sampling; and latent-space interpolation that preserves phase relationships. During both training and inference, modality-dropout policies may treat absent or low-quality channels as masked inputs while preserving operation of the encoder.
[0106] The hardware timestamping 512 may be connected to drift compensation 514 that accounts for clock skew accumulation over extended monitoring periods. The drift compensation 514 may implement correction models that characterize timing differences between distributed sensor nodes and apply correction factors based on temperature-dependent frequency variations and aging effects in sensor timing circuits. The drift compensation 514 may monitor clock drift variations caused by temperature changes, battery voltage fluctuations, and crystal oscillator aging to maintain temporal coherence throughout extended physiological monitoring sessions.
[0107] The synchronization module 510 may be connected to a preprocessing module 516 that receives the synchronized signals and applies signal conditioning operations to improve signal quality and reduce noise artifacts. The preprocessing module 516 may implement modality-specific processing pipelines that account for the different noise characteristics, frequency content, and artifact patterns associated with BCG, ECG, and PPG signal measurements. The preprocessing module 516 may include adaptive parameter adjustment mechanisms that modify filtering characteristics based on signal quality assessments and environmental conditions.
[0108] Preprocessing pipelines may employ activity-aware artifact handling. Motion context derived from inertial measurements may gate denoising or normalize thresholds, and quality estimators may adapt filter characteristics to preserve physiological content. In some embodiments, the system may selectively resample or normalize channels to stabilize downstream embedding generation during periods of motion.
[0109] A definitions section is provided for clarity. As used herein, a 'shared embedding' refers to a learned vector representation produced from synchronized biosignals and suitable for multiple downstream tasks without task-specific feature engineering. A 'windowed segment' refers to a contiguous segment of input signals processed with overlap to enable comprehensive analysis. An 'out-of-distribution (OOD) gate' refers to inference-time logic that estimates whether current embeddings deviate from learned distributions and conditionally adapts output or resource policies.
[0110] The wavelet denoising 518 configured to implement wavelet decomposition using configurable wavelet types. The wavelet denoising 518 may provide different time-frequency localization characteristics suitable for specific physiological signal morphologies, where different wavelet types may provide varying support characteristics for transient signal features, balanced time-frequency resolution for cardiac waveform analysis, and enhanced frequency selectivity for respiratory signal components. The wavelet denoising 518 may implement adaptive thresholding that applies different threshold values to individual signal components based on statistics computed over configurable analysis windows, enabling selective noise reduction while preserving physiological signal components across different frequency bands.
[0111] Unless expressly recited in the claims, any numerical values, ranges, sampling frequencies, window lengths, mask spans, kernel sizes, or performance targets described herein are presented as non-limiting examples. Equivalent alternatives, supersets, or subsets are contemplated, and implementations may vary according to hardware, firmware, software, or clinical context without departing from the scope of the invention.
[0112] The wavelet denoising 518 may be connected to motion artifact rejection 520 that suppresses noise during detected movement periods. The motion artifact rejection 520 may implement accelerometer-gated suppression algorithms configured to suppress noise during detected movement periods exceeding a configurable acceleration magnitude threshold, where accelerometer data streams provide contextual information about patient movement that may contaminate physiological signal measurements. The motion artifact rejection 520 may utilize threshold-based detection algorithms that monitor acceleration measurements to identify periods of patient motion, body position changes, or external disturbances that may affect signal quality across any of the sensor modalities.
[0113] The preprocessing module 516 may be connected to a transformer encoder 522 executing on a processor and configured to generate quantized shared embeddings from the preprocessed signals. The transformer encoder 522 may process temporal sequences to capture physiological patterns across different time scales and modalities. The transformer encoder 522 may implement memory-efficient processing architectures that utilize windowed segments with configurable duration and hop intervals to provide sufficient temporal context for physiological pattern recognition.
[0114] The transformer encoder may be replaced with convolutional neural network variants including ResNet architectures that implement residual blocks with convolutional layers, normalization, and activation functions, DenseNet architectures that implement dense connectivity patterns where each layer receives inputs from all preceding layers creating feature reuse mechanisms, or other CNN architectures that utilize sequences of convolutional layers with various kernel sizes followed by pooling operations. Graph neural networks may model physiological signals as graph structures where temporal samples represent nodes and edges encode physiological relationships, implementing graph convolutional operations that aggregate information from neighboring temporal samples and graph attention mechanisms that weight the importance of different temporal samples based on learned attention coefficients. Sequence networks including LSTM or GRU networks may process physiological signals as temporal sequences where hidden states maintain information about previous signal samples, implementing bidirectional processing to capture both causal and anti-causal temporal dependencies and gating mechanisms to selectively retain or forget information about previous physiological states.
[0115] The transformer encoder 522 includes an attention mechanism 524 configured to implement attention mechanisms and grouped operations that optimize computational efficiency while preserving the ability to capture temporal dependencies and cross-modal relationships. The attention mechanism 524 may enable the model to focus on relevant temporal relationships and cross-modal correlations, while grouped operations may partition attention heads into independent processing groups to optimize computational efficiency and enable parallel computation. The attention mechanism 524 may implement masking mechanisms that control information flow during training and inference.
[0116] The attention mechanism 524 may implement differential attention variants that compute attention weights based on differences between query and key representations using distance metrics, enabling focus on physiological signal changes or transitions rather than absolute signal values. Grouped attention mechanisms may partition attention heads into independent groups that process different aspects of physiological signals, where each group utilizes different query, key, and value projection matrices optimized for specific physiological characteristics such as cardiac rhythm patterns, respiratory modulation, or autonomic nervous system activity, optimizing computational efficiency while enabling parallel processing on multi-core processors or specialized hardware accelerators.
[0117] The attention mechanism 524 may be connected to an embedding generator 526 configured to generate quantized shared embeddings with configurable dimensionality using quantization with per-channel scale and zero-point parameters. The embedding generator 526 may implement quantization algorithms that reduce memory bandwidth requirements and enable deployment on resource-constrained computing platforms while maintaining numerical precision sufficient for physiological pattern recognition. The embedding generator 526 may utilize per-channel quantization parameters that account for different dynamic ranges and statistical distributions across embedding dimensions, enabling efficient compression while preserving discriminative information for downstream task processing.
[0118] The embedding generator 526 may produce embeddings with variable dimensions, where lower-dimensional embeddings may be generated for resource-constrained deployment scenarios and higher-dimensional embeddings may be generated for applications requiring detailed physiological characterization or complex multi-task learning scenarios. The embedding generator may implement adaptive dimensionality selection mechanisms that adjust embedding dimensions based on available computational resources, signal quality, or specific application requirements, enabling flexible deployment across different hardware platforms and use cases.
[0119] The transformer encoder 522 may be connected to a task execution module 528 that receives the quantized shared embeddings and generates clinical outputs for multiple physiological monitoring applications. The task execution module 528 may implement neural network modules that process the shared embeddings to generate task-specific predictions while sharing computational resources and learned representations across multiple clinical monitoring objectives. The task execution module 528 may include confidence scoring mechanisms that quantify prediction reliability based on embedding quality metrics, signal characteristics, and historical performance data.
[0120] The system may operate in single-modality or reduced-sensor configurations when circumstances limit sensor availability, using modality-aware preprocessing and masked inputs to the encoder where missing modalities may be ignored or imputed only when confidence criteria are met. In multi-occupant environments, embeddings may be entity-scoped and device control namespaced to identified occupants to prevent cross-actuation, where conflicts for shared actuators are resolved via policy-based arbitration that prioritizes safety-critical outputs. Closed-loop safety policies may incorporate out-of-distribution gating and confidence thresholds, where actuation commands are inhibited when embeddings are out-of-distribution or confidence falls below thresholds, affected devices revert to safe defaults, and new baselines are acquired before re-enabling control.
[0121] The task execution module 528 comprises a cardiac arrhythmia detection head 530 configured to implement neural networks trained on embedding representations to identify abnormal heart rhythm patterns. The cardiac arrhythmia detection head 530 may identify arrhythmia patterns including atrial fibrillation, ventricular tachycardia, and premature ventricular contractions based on embedding analysis. The cardiac arrhythmia detection head 530 may implement classification algorithms that process embedding vectors to generate arrhythmia severity scores and provide clinical alerts for healthcare providers.
[0122] A heart failure monitoring head 532 may be configured to implement embedding-based classifiers to assess cardiac dysfunction severity and track disease progression. The heart failure monitoring head 532 may classify heart failure stages based on physiological patterns captured in embedding representations including cardiac output variations, fluid retention indicators, and exercise tolerance metrics. The heart failure monitoring head 532 may implement trend analysis mechanisms that monitor embedding patterns over time to detect disease progression and treatment response.
[0123] A sleep disorder assessment head 534 may be configured to implement embedding-based models to identify sleep apnea events, sleep stage transitions, and other sleep-related physiological disturbances. The sleep disorder assessment head 534 may enable detection of sleep apnea severity, sleep fragmentation patterns, and circadian rhythm disorders based on the shared embedding representations generated from the synchronized multimodal biosignals. The sleep disorder assessment head 534 may implement temporal analysis mechanisms that track sleep quality metrics and generate treatment recommendations for sleep disorders. Additionally, smart bed applications may utilize embeddings from the sleep disorder assessment head to optimize sleep environment conditions including bed positioning, ambient lighting, and temperature control, though these represent secondary applications compared to the primary clinical monitoring functions.
[0124] The embedding generator 526 may be connected to the cardiac arrhythmia detection head 530, heart failure monitoring head 532, and sleep disorder assessment head 534, providing shared embeddings for multiple clinical monitoring tasks. The connection architecture may enable parallel processing of multiple task heads using the same embedding representations, reducing computational overhead compared to independent task-specific models while maintaining specialized processing capabilities for each clinical monitoring objective. The shared embedding approach may enable transfer learning between related physiological monitoring tasks and improve generalization performance through multi-task learning during model training.
[0125] The transformer encoder 522 may also be connected to a circular embedding store 536 configured for storage of the quantized shared embeddings with integrity verification and time-to-live (TTL) coalescing. The circular embedding store 536 may implement ring buffer data structures with fixed memory allocation to prevent unbounded memory growth during monitoring applications, where older embeddings may be automatically overwritten when storage capacity is reached. The circular embedding store 536 may include integrity verification mechanisms that provide data integrity verification for stored embeddings, enabling detection of memory corruption or transmission errors that could compromise downstream analysis accuracy, and TTL coalescing that aggregates multiple embedding vectors over configurable time intervals before storage or transmission to reduce storage overhead and network bandwidth requirements while maintaining temporal resolution sufficient for clinical monitoring applications.
[0126] The vectorized database may implement specialized indexing structures optimized for high-dimensional embedding vectors, where approximate nearest neighbor search algorithms may enable rapid similarity queries across large embedding datasets. The similarity querying capability may enable identification of physiological patterns that resemble historical measurements, where similarity metrics may be computed between embedding vectors to identify periods with similar cardiovascular activity, respiratory patterns, or autonomic nervous system states, supporting clinical applications including trend analysis, baseline establishment, and comparative assessment of physiological changes over time.
[0127] The system may further comprise an API interface configured to expose embedding generation and retrieval functions for external applications. An API surface may expose the embedding generation and retrieval functions with formats suitable for local or remote consumers, where inputs may include synchronized signal windows or device-timestamped fragments and outputs may include shared embeddings, quality or out-of-distribution indicators, and optional similarity-query handles into the time-keyed store. Fresh embeddings may be generated for all instances including older datasets to ensure backward compatibility without requiring version control mechanisms for embedding representations, where the fresh embedding generation may apply updated encoder parameters to historical physiological data, enabling consistent analysis across different data collection periods and sensor hardware configurations.
[0128] The multimodal biosignal representation learning system described herein may provide several technical advantages over conventional physiological monitoring approaches.
[0129] The system may reduce memory overhead by sharing learned representations across multiple downstream tasks, decreasing total memory consumption from multiple gigabytes required for separate task-specific models to hundreds of megabytes for a single shared embedding model with lightweight task heads. This reduction in memory requirements may enable deployment on resource-constrained edge computing platforms including mobile phones, wearable devices, and embedded systems that would otherwise be unable to support comprehensive physiological monitoring capabilities.
[0130] The system may improve processing efficiency through unified processing of multimodal inputs in a single forward pass, eliminating sequential processing delays associated with independent modality-specific pipelines. The shared embedding approach may reduce computational redundancy by extracting common physiological features once rather than repeatedly across multiple task-specific models, resulting in lower power consumption and extended battery life for portable monitoring devices.
[0131] The self-supervised training approach may reduce dependence on costly labeled medical data by learning generalized physiological representations from unlabeled multimodal biosignal recordings. This label efficiency may accelerate development of new clinical monitoring applications by reducing the annotation burden typically required for supervised learning approaches, where expert-annotated physiological datasets are expensive and time-consuming to obtain.
[0132] The system may enable efficient transfer learning between related physiological monitoring tasks through fine-tuning of shared embeddings with task-specific heads, reducing training time and data requirements for new clinical applications. A single embedding model may support multiple prediction tasks including respiratory rate estimation, sleep staging, anomaly detection, and stress monitoring without requiring separate model development and validation for each application.
[0133] The hardware-timestamped synchronization with drift compensation may improve temporal alignment accuracy across heterogeneous sensor systems, enabling coherent multimodal analysis of physiological signals acquired from sensors with different hardware characteristics, sampling frequencies, and communication protocols. The reduced temporal misalignment may improve cross-modal learning during training and reduce false alerts during inference by ensuring that physiological events are correctly correlated across different measurement modalities.
[0134] The system may improve generalization performance across different patient populations, sensor configurations, and deployment environments through self-supervised learning on diverse unlabeled datasets. The learned representations may capture fundamental physiological patterns that transfer across different sensor manufacturers, device types, and clinical settings without requiring retraining or recalibration for each deployment scenario.
[0135] The circular embedding store with integrity verification and time-to-live coalescing may reduce storage overhead and network bandwidth requirements while maintaining temporal resolution sufficient for clinical monitoring applications. The integrity verification mechanisms may detect memory corruption or transmission errors that could compromise downstream analysis accuracy, improving reliability of physiological monitoring in clinical environments.
[0136] The out-of-distribution detection capabilities may improve system reliability by identifying physiological signals that deviate significantly from training data distributions and conditionally adapting output or resource policies. This reliability-preserving control path may suppress or defer outputs when embedding quality is uncertain, reducing spurious alerts and improving clinician trust in automated monitoring systems.
[0137] The modular architecture may enable independent scaling and optimization of individual processing components based on computational resource availability and clinical monitoring requirements. The system may operate in single-modality or reduced-sensor configurations when circumstances limit sensor availability, using modality-aware preprocessing and masked inputs to preserve operation when some signal modalities are unavailable or degraded.
[0138] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims.
, Claims:We claim:
1. A method for generating physiological embeddings for reduced computational overhead, the method comprising:
receiving, from sensors, physiological signals corresponding to a plurality of modalities;
time-aligning the physiological signals to produce synchronized signals;
preprocessing the synchronized signals by executing modality-aware signal-conditioning operation to produce preprocessed signals;
encoding windowed segments of the preprocessed signals to generate a shared embedding for the windowed segments;
storing the shared embedding in a machine-readable store; and
exposing the shared embedding for consumption by one or more downstream computational processes.
2. The method of claim 1, wherein the plurality of modalities comprise at least two of ballistocardiogram (BCG), electrocardiogram (ECG), and photoplethysmogram (PPG).
3. The method of claim 1, wherein time-aligning comprises associating the physiological signals with acquisition timestamps and estimating and compensating for inter-sensor clock drift.
4. The method of claim 1, wherein preprocessing comprises at least one of band-pass filtering with modality-aware frequency selection and wavelet-domain denoising with adaptive thresholds.
5. The method of claim 1, wherein encoding comprises using a streaming encoder having an attention mechanism and/or a convolutional mechanism with a bounded temporal receptive field to provide deterministic latency during inference.
6. The method of claim 1, further comprising representing each shared embedding in a quantized format and storing quantization parameters associated with the shared embedding.
7. The method of claim 1, wherein storing comprises writing the shared embedding into a circular memory structure and batching uploads according to a time-to-live (TTL) policy while verifying an integrity check prior to transmission.
8. The method of claim 1, further comprising training the encoder using one or more of masked-segment modeling, cross-modal prediction, and contrastive alignment to learn generalized physiological representations.
9. The method of claim 1, further comprising implementing out-of-distribution detection by computing a distance measure in an embedding space and suppressing outputs when current embeddings exceed learned distribution boundaries.
10. The method of claim 1, wherein exposing the shared embedding comprises providing an interface that receives streaming multi-channel frames with associated metadata and outputs per-window embedding vectors with optional quantization parameters and quality indicators.
11. The method of claim 1, wherein time-aligning and/or preprocessing comprise establishing a common temporal reference across modalities using at least one of resampling, attention-based alignment with learned temporal offsets, differentiable time-warping, or latent-space interpolation.
12. The method of claim 1, further comprising operating in a single-modality mode responsive to unavailable or low-quality modalities, wherein masked inputs preserve streaming operation of the encoder.
13. The method of claim 1, further comprising retrieving time-keyed embeddings using similarity search to surface physiologically similar epochs for trend analysis, baseline comparison, or cohorting.
14. The method of claim 1, further comprising generating a control signal from the shared embedding and actuating at least one of a respiratory support device, a bed actuator, an HVAC controller, a lighting controller, a haptic output device, and a notification manager.
15. A system comprising at least one processor and a memory storing instructions that, when executed by the at least one processor, cause the system to:
receive physiological signals from one or more sensors;
time-align the physiological signals to produce synchronized signals;
preprocess the synchronized signals by executing at least one modality-aware signal-conditioning operation to produce preprocessed signals;
encode windowed segments of the preprocessed signals to generate a shared embedding for the windowed segments;
store the shared embedding in a machine-readable store; and
expose the shared embedding for consumption by one or more downstream computational processes.
16. The system of claim 15, wherein the instructions further cause the system to associate the physiological signals with hardware timestamps and estimate and compensate inter-sensor clock drift.
17. The system of claim 15, wherein the instructions further cause the system to perform accelerometer-gated motion artifact handling configured to suppress noise during detected movement.
18. The system of claim 15, wherein the encoder is configured for streaming inference with attention and/or convolution having a bounded temporal receptive field.
19. The system of claim 15, wherein the instructions further cause the system to evaluate a candidate model in shadow against a production model and to initiate rollback responsive to a violation of performance criteria.
20. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:
receive, from one or more sensors, physiological signals from one or more modalities;
time-align the physiological signals to produce synchronized signals;
preprocess the synchronized signals by executing at least one modality-aware signal-conditioning operation to produce preprocessed signals;
encode windowed segments of the preprocessed signals to generate a shared embedding for the windowed segments;
store the shared embedding in a machine-readable store; and
expose the shared embedding for consumption by one or more downstream computational processes.