Description:DESCRIPTION:
Field of the invention:
The present invention relates to the interdisciplinary field of biomedical engineering, signal processing, and artificial intelligence, specifically focusing on the intelligent and real-time detection of life-threatening cardiac arrhythmias such as Ventricular Fibrillation (VF) through electrocardiogram (ECG) signal analysis. The invention introduces a Dynamic Learning Framework that harnesses deep neural network ensembles—comprising Autoencoders, Gradient Boosting Machines (GBM), and pre-trained Recurrent Neural Networks (RNNs)—to accurately identify ventricular fibrillation patterns even in noisy, real-world ECG data. By integrating advanced preprocessing techniques like Kalman filtering and wavelet denoising with robust R-peak and QRS complex segmentation, the invention enables precise feature extraction and classification. This system is adaptable to both clinical and wearable health-monitoring environments, offering real-time diagnostic capability while addressing the limitations of conventional VF detection methods in terms of accuracy, sensitivity, and computational feasibility.

Background of the invention:
[0001] Ventricular fibrillation (VF) is one of the most dangerous cardiac arrhythmias, characterized by rapid and erratic electrical impulses that cause the heart to quiver instead of contracting properly. If left untreated, VF can result in sudden cardiac arrest and death within minutes. Early detection and intervention are therefore critical for improving patient survival rates. Traditionally, VF is diagnosed through manual analysis of electrocardiogram (ECG) waveforms by trained clinicians. However, this process is time-consuming, prone to human error, and often ineffective in high-noise environments such as ambulance care or mobile monitoring scenarios. These limitations underscore the urgent need for a reliable, automated solution capable of accurately identifying VF in real-time across a range of practical settings.

[0002] In recent years, machine learning and deep learning models have been increasingly explored for their potential in automatic arrhythmia detection. While convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have shown promise in extracting relevant ECG features, they often struggle with performance consistency when faced with noisy or incomplete signals. Moreover, deep learning models generally require large volumes of labeled data and high computational power, making them less suitable for deployment on low-resource devices like wearable health monitors or mobile ECG scanners. These technical barriers have limited their adoption in real-world scenarios where speed, accuracy, and efficiency are essential.

[0003] Additionally, many existing systems focus on either signal segmentation or classification, failing to offer a unified pipeline that incorporates all stages of VF detection—from preprocessing and noise reduction to robust feature extraction and final classification. Segmenting ECG waveforms accurately, particularly detecting R-peaks and delineating QRS complexes, is fundamental for precise diagnosis. However, standalone segmentation or classification methods are often inadequate in providing holistic, high-confidence predictions, especially under time-sensitive conditions. Furthermore, these systems typically lack adaptability, relying on static models that do not generalize well across patient demographics or varying signal conditions.

[0004] Given these limitations, there is a pressing demand for a dynamic, intelligent, and real-time VF detection system that can function effectively in clinical and mobile health applications. The present invention addresses this need through an integrated framework that combines signal preprocessing, advanced segmentation techniques, and ensemble-based classification using deep learning and gradient boosting. By employing transfer learning and hybrid model architectures, the system reduces dependence on extensive labeled datasets and enables adaptive learning across different patient profiles. This invention represents a significant advancement in cardiovascular diagnostics, aiming to enhance early VF detection, reduce false positives, and provide a scalable, deployable solution for diverse healthcare environments.

Objectives of the invention:
[0005] The primary objective of the present invention is to develop a robust, dynamic learning framework capable of accurately detecting ventricular fibrillation (VF) using electrocardiogram (ECG) signals. This system aims to overcome the limitations of traditional diagnostic approaches and existing machine learning models by integrating advanced preprocessing, precise signal segmentation, and ensemble-based deep learning techniques. The framework is designed to function effectively in both clinical and mobile health monitoring environments, enabling real-time and reliable detection of VF with minimal human intervention.

[0006] A critical goal of this invention is to enhance the quality of ECG signal analysis through intelligent preprocessing methods such as wavelet-based denoising and Kalman filtering. These techniques help remove baseline wander and random noise, improving signal clarity and preserving important waveform characteristics essential for accurate diagnosis. The invention further incorporates adaptive segmentation algorithms, such as Pan-Tompkins and template-matching methods, for precise detection of R-peaks and delineation of QRS complexes, which form the foundation for reliable arrhythmia classification.

[0007] Another key objective is to develop a hybrid neural architecture that combines the feature extraction capabilities of autoencoders and pre-trained recurrent neural networks (RNNs) with the predictive power of Gradient Boosting Machines (GBMs). This ensemble approach allows the system to capture both spatial and temporal ECG features effectively and boosts classification accuracy by leveraging diverse learning paradigms. The use of transfer learning further enhances model generalizability, reducing the need for large, labeled training datasets and making the system applicable across a wide range of patient demographics and ECG signal variations.

[0008] Additionally, the invention aims to provide a practical and scalable solution for continuous cardiac monitoring through its real-time diagnostic capabilities and support for low-resource hardware environments. This includes deployment on mobile devices and wearable health systems, enabling proactive cardiac care in remote or emergency scenarios. The system’s lightweight architecture and efficient processing pipeline are optimized for speed, responsiveness, and adaptability without sacrificing diagnostic accuracy.

[0009] Finally, the invention is intended to improve clinical decision-making by offering intuitive visualizations and confidence metrics alongside diagnostic outputs. By overlaying predictions on ECG waveforms and generating VF likelihood scores and interval-based confidence indicators, the system assists clinicians in making faster and more informed decisions. These features contribute to a reduction in diagnostic errors, enhance the effectiveness of early intervention, and ultimately support improved patient outcomes in cases of life-threatening arrhythmias like ventricular fibrillation.

Summary of the invention:
[0010] The present invention introduces a dynamic learning framework specifically engineered to detect ventricular fibrillation (VF) from electrocardiogram (ECG) signals using an ensemble of deep learning models. This invention addresses key limitations of existing detection systems, such as low robustness to noise, high computational demands, and poor generalization. The core innovation lies in combining advanced signal preprocessing techniques, intelligent segmentation methods, and a hybrid ensemble of neural models, including Autoencoders, pre-trained Recurrent Neural Networks (RNNs), and Gradient Boosting Machines (GBMs).
[0011] The invention starts with a comprehensive preprocessing phase, where ECG signals undergo wavelet-based denoising to suppress baseline wander and external noise, followed by Kalman filtering to smooth the waveform while maintaining temporal fidelity. This preprocessing significantly enhances the signal-to-noise ratio, which is critical for reliable feature extraction in clinical and mobile ECG recordings. These clean signals are then subjected to adaptive segmentation algorithms that identify R-peaks and accurately delineate QRS complexes, providing localized cardiac event boundaries essential for arrhythmia detection.
[0012] Following segmentation, the invention leverages a multi-model feature extraction and classification process. A stacked autoencoder compresses the ECG data into latent vectors, preserving waveform morphology, while a pre-trained RNN captures long-range dependencies across cardiac cycles. These learned features are passed into a GBM classifier that integrates outputs from both neural components to provide high-confidence VF detection. The ensemble approach maximizes classification accuracy and minimizes false positives by combining temporal and spatial insights.
[0013] The model also incorporates transfer learning, allowing the RNN component to be fine-tuned using smaller, VF-specific datasets. This dramatically improves the system's adaptability across different patient profiles and clinical conditions without requiring massive labeled datasets. The system outputs not only a binary prediction (VF detected or not), but also a VF confidence score, prediction overlays on the ECG trace, and confidence intervals, enhancing interpretability and clinical trustworthiness.
[0014] Designed for real-time use in both clinical and wearable environments, this invention offers a compact and resource-efficient implementation. Its lightweight architecture allows deployment in mobile ECG monitoring devices and remote healthcare systems. Ultimately, this invention empowers early VF detection with a high degree of accuracy, reliability, and usability—potentially saving lives by enabling timely medical intervention in critical cardiac events.
[0015] Further, objects and advantages of the present invention will be apparent from a study of the following portion of the specification, the claims, and the attached drawings.

Detailed description of drawings:
[0016] The invention's drawings illustrate the structural and functional layout of the dynamic learning framework for ECG-based detection of ventricular fibrillation. The first figure demonstrates the complete system pipeline, beginning with the data acquisition block that collects ECG signals from wearable or clinical devices. This signal is routed to the preprocessing module, which implements wavelet denoising and Kalman filtering to remove noise and baseline wander. The signal is then passed to the segmentation module that detects R-peaks and identifies QRS complex boundaries using a modified Pan-Tompkins algorithm. Following segmentation, the signal is forwarded to the neural ensemble framework comprising an autoencoder and a pre-trained RNN, which together extract both spatial and temporal features.
[0017] The subsequent figure highlights the architectural composition of the RNN block, including the input layer that accepts sequential ECG segments, LSTM layers for temporal feature extraction, and a fully connected output layer that predicts the likelihood of ventricular fibrillation. Another drawing showcases the QRS segmentation process, visually indicating how the system identifies critical intervals around the R-peak to isolate the QRS complex. This graphical representation aids in understanding how accurate signal interval detection contributes to more precise classification. The final output layer delivers a decision report, including VF likelihood, confidence intervals, and an overlay of classification on the ECG waveform, enabling easy interpretation by clinicians or automated health monitoring systems.

Detailed invention disclosure:
[0018] Designed for real-time use in both clinical and wearable environments, this invention offers a compact and resource-efficient implementation. Its lightweight architecture allows deployment in mobile ECG monitoring devices and remote healthcare systems. Ultimately, this invention empowers early VF detection with a high degree of accuracy, reliability, and usability—potentially saving lives by enabling timely medical intervention in critical cardiac events.
[0019] Signal Acquisition begins with the input of raw ECG signals obtained from either clinical ECG machines or portable/wearable devices. These signals often suffer from various types of noise, including baseline wander, motion artifacts, and powerline interference. Hence, the first phase of the invention is robust preprocessing aimed at cleaning the signals without compromising critical features.
[0020] In the preprocessing stage, the system utilizes Wavelet Denoising to remove high-frequency and low-frequency noise. By decomposing the ECG signal into various resolution levels, it effectively suppresses noise while retaining key components of the QRS complex. This is followed by Kalman Filtering, which models the ECG waveform as a state-space process and iteratively predicts and corrects the signal to smooth fluctuations caused by transient disturbances.
[0021] The next stage is ECG Segmentation, which involves precise identification of cardiac waveform components. The Pan-Tompkins algorithm is employed for detecting R-peaks, utilizing amplitude thresholding and digital filters. Once R-peaks are located, QRS complexes are segmented using template matching and derivative-based detection, ensuring accurate demarcation of vital cardiac events.
[0022] After segmentation, feature extraction is performed using a combination of deep learning models. An Autoencoder compresses ECG segments into latent representations that preserve signal morphology. This reduces dimensionality while retaining critical diagnostic information. Simultaneously, a pre-trained Recurrent Neural Network (RNN), such as a Long Short-Term Memory (LSTM) model, captures temporal patterns and sequential dependencies across heartbeats.
[0023] The outputs from the Autoencoder and RNN are merged and fed into a Gradient Boosting Machine (GBM). GBM serves as the final classifier, synthesizing both spatial and temporal features to make accurate predictions about the presence of VF. This ensemble approach boosts overall performance, combining the strengths of deep learning with gradient boosting’s robustness and interpretability.
[0024] A crucial innovation in this invention is the use of Transfer Learning for the RNN. The RNN is initially trained on a large dataset of ECG signals and then fine-tuned with VF-specific data. This strategy significantly reduces the requirement for large labeled VF datasets and allows the system to generalize better across various patient demographics and ECG devices.
[0025] The output layer provides multiple forms of decision support. It includes a binary prediction (VF or normal), a confidence score indicating the system’s certainty, and graphical overlays on the ECG waveform showing predicted abnormalities. These overlays can be visually examined by medical professionals, increasing the system’s transparency and trustworthiness in clinical environments.
[0026] The system is optimized for real-time processing, with a lightweight design suitable for embedded systems and wearable devices. It operates with low computational resources, making it ideal for mobile health applications. Additionally, the modular design allows the system to be scaled or updated with newer models and algorithms as advancements in ECG analysis evolve.
[0027] Overall, this invention provides a comprehensive, accurate, and deployable solution for VF detection in ECG signals. By integrating denoising, segmentation, deep learning, and ensemble classification in a unified architecture, it bridges the gap between academic research and real-world application. It ensures early intervention, reduces false positives, and offers high reliability for continuous patient monitoring, ultimately contributing to better cardiac health outcomes. , Claims:CLAIMS:
I / We Claim:
1. A ventricular fibrillation detection system based on ECG signal analysis and including a pre-processing unit, a segmentation unit, and an ensemble classification model.
2. The system of claim 1, in which the pre-processing module uses wavelet denoising and Kalman filtering.
3. In the system of claim 1, the segmentation module detects R-peaks and segments the QRS complexes.
4. The system of claim 1, whereby the classification model includes: an autoencoder, a pre-trained RNN and a GBM ensemble.
5. The system of claim 1, where RNN is fine-tuned with the help of transfer learning, in order to recognize ventricular fibrillation patterns.
6. In the claim 1, the output of the system comprises a VF confidence score and visualization of labelled waveform.
7. The system of claim 1, is real-time, low-resource such as mobile ECG system and wearable ECG system.