1. A system (100) for early cardiovascular disease detection from electrocardiogram (ECG) signals using an intelligent explainable artificial intelligence framework with evolutionary deep learning optimization, the system comprising: a) one or more sensors configured to acquire ECG signals and optional physiological parameters; a communication interface; b) a memory storing executable instructions; c) a processor operatively coupled to the memory and the communication interface, wherein the processor is configured to: a) preprocess the acquired ECG signals using noise filtering, normalization, and segmentation techniques; b) perform adaptive bio-signal fusion by integrating ECG signals with additional physiological parameters to generate a unified data representation; c) execute an evolutionary deep learning optimization process comprising genetic algorithms and neural architecture search to dynamically determine an optimized model architecture; d) analyze the unified data representation using the optimized model to detect cardiovascular abnormalities; e) generate interpretable diagnostic outputs using an explainable artificial intelligence module mapped to ECG waveform components.

2. The system (100) as claimed in claim 1, wherein the adaptive bio-signal fusion comprises an attention-based mechanism configured to dynamically assign weights to ECG and auxiliary physiological signals based on signal reliability and contextual relevance.

3. The system (100) as claimed in claim 1, wherein the processor is further configured to perform temporal disease progression prediction using time-series modeling and graph-based analysis to estimate future cardiovascular risk.

4. The system (100) as claimed in claim 1, wherein the processor is further configured to enable federated learning by aggregating model updates from multiple decentralized sources without sharing raw patient data, thereby preserving data privacy.

5. The system (100) as claimed in claim 1, further comprising an edge or neuromorphic processing unit configured to perform real-time, low-power inference, and a clinical decision support module configured to generate alerts, risk scores, and diagnostic reports for end users.

6. A method for early cardiovascular disease detection from electrocardiogram (ECG) signals using an intelligent explainable artificial intelligence framework with evolutionary deep learning optimization, the method comprising: a) acquiring ECG signals and optional physiological parameters from one or more sensing devices; preprocessing the acquired signals using noise filtering, normalization, and segmentation; b) performing adaptive bio-signal fusion to generate a unified physiological data representation; executing an evolutionary deep learning optimization process to dynamically determine an optimized model architecture; c) analyzing the unified data representation using the optimized model to detect cardiovascular abnormalities; d) generating interpretable diagnostic outputs using an explainable artificial intelligence module.

7. The method as claimed in claim 6, wherein performing adaptive bio-signal fusion comprises applying an attention-based mechanism to combine ECG signals with additional physiological data based on signal quality and contextual importance.

8. The method as claimed in claim 6, further comprising predicting cardiovascular disease progression using temporal analysis of historical and real-time data through time-series and graph-based modeling techniques.

9. The method as claimed in claim 6, further comprising performing federated learning by aggregating model updates from multiple distributed nodes while maintaining data privacy and security.

10. The method as claimed in claim 6, further comprising generating real-time alerts, risk scores, and clinician-readable diagnostic reports, and continuously updating model parameters using feedback-driven learning mechanisms.

Description:TECHNICAL FIELD
[0001] The present disclosure relates to a system and method for the early detection of cardiovascular disease utilizing electrocardiogram (ECG) signals. Specifically, it describes an intelligent, explainable artificial intelligence (AI)-driven framework that incorporates evolutionary deep learning optimization, adaptive bio-signal processing, and real-time clinical decision support to achieve precise and interpretable cardiac diagnosis.

BACKGROUND
[0002] As healthcare and biomedical engineering change quickly, it has become very important to find cardiovascular diseases (CVDs) early because heart-related diseases are becoming more common around the world. Electrocardiogram (ECG) signals are commonly utilized as a non-invasive diagnostic instrument for the surveillance of cardiac activity; nevertheless, precise interpretation of ECG signals, particularly for early-stage anomalies, necessitates considerable expertise and ongoing monitoring. As artificial intelligence and machine learning techniques have improved, more and more people are interested in automating ECG analysis to make diagnosis faster, cut down on mistakes made by people, and allow for real-time monitoring in both clinical and remote healthcare settings.
[0003] Standard approaches for ECG-based cardiovascular disease detection predominantly depend on established signal processing methodologies and fixed machine learning or deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These systems frequently function as black-box models that lack transparency and interpretability, consequently hindering their acceptance among clinicians. Also, these kinds of methods usually only look at ECG signals and don't include other physiological parameters, which makes them less accurate and less reliable for diagnosis. Current systems are also not flexible enough to work with different types of patients, don't dynamically optimize model architectures, and don't give any hints about how diseases will progress. Furthermore, issues pertaining to data privacy, regulatory adherence, and real-time implementation in resource-limited settings continue to be insufficiently resolved. To solve these technical issues, we need a more advanced system that combines explainable AI, adaptive learning methods, multi-modal data fusion, and optimized model architectures to make cardiovascular diagnosis more reliable and clinically useful.
[0004] Consequently, there is a necessity for a system and methodology for the early detection of cardiovascular disease from ECG signals that integrates intelligent explainable AI, evolutionary deep learning optimization, adaptive physiological signal fusion, and privacy-preserving real-time processing, thus facilitating precise, interpretable, and scalable cardiac diagnostic solutions appropriate for both clinical and remote healthcare contexts.
SUMMARY
[0005] In an embodiment, a method for the early detection of cardiovascular disease from electrocardiogram (ECG) signals, utilizing an intelligent explainable artificial intelligence framework enhanced by evolutionary deep learning optimization. The method involves a processor receiving multi-lead ECG signals and possibly other physiological parameters from one or more sensing devices. These parameters could be photoplethysmography (PPG), blood pressure variability, or oxygen saturation data. The processor uses adaptive filtering, noise reduction, and signal segmentation to process the signals it gets and find cardiac cycles. The processor also uses an attention-based fusion mechanism to do adaptive multi-modal bio-signal fusion and make a single physiological representation. Then, the processor runs an evolutionary deep learning optimization process that uses a genetic algorithm-based neural architecture search to choose and improve model architectures, hyperparameters, and feature representations for ECG analysis. Then, the processor looks at the optimized model output to find heart problems and uses an explainable AI module to create diagnostic outputs that can be understood by mapping predictions to clinically relevant ECG waveform segments. The processor also uses time-series analysis and graph-based modeling to predict how a disease will progress over time and estimate future cardiac risk. Additionally, the processor makes privacy-preserving federated learning possible by combining decentralized model updates without sharing raw patient data. The processor also makes real-time alerts, diagnostic reports, and insights that doctors can read. It keeps improving the model by using self-learning mechanisms that are based on feedback, which makes it possible to accurately, adaptively, and clearly detect cardiovascular disease.
[0006] In an embodiment, a system for the early detection of cardiovascular disease from electrocardiogram (ECG) signals, utilizing an intelligent explainable artificial intelligence framework integrated with evolutionary deep learning optimization. The system includes one or more sensors that can pick up ECG signals and optional physiological data, a communication interface, a memory that stores executable instructions, and a processor that is connected to both the memory and the communication interface. The processor is set up to use adaptive filtering and segmentation techniques to preprocess the signals that have been received. It also runs an adaptive bio-signal fusion module to combine ECG data with other physiological inputs. The processor is also set up to use genetic algorithms and neural architecture search to create and improve deep learning models for classifying ECG signals. The processor is also set up to run an explainable AI module that makes outputs that can be understood and linked to specific parts of the ECG waveform for clinical validation. The processor is also set up to do temporal disease progression analysis using advanced predictive modeling techniques and to support federated learning for secure and privacy-preserving model training across distributed data sources. The system also has a neuromorphic or edge processing unit for real-time low-power inference and a clinical decision support module that can make alerts, risk scores, and diagnostic reports. The processor is also set up to keep audit trails that follow the rules and to constantly update model parameters through feedback-driven learning. This makes it possible to create an intelligent, scalable, and clinically reliable system for finding cardiovascular disease.
BRIEF DESCRIPTION OF DRAWINGS
[0007] The depicted illustrations illustrate a few embodiments of the systems, methods and/or aspects of the disclosure. A person of ordinary skill in the art will appreciate that the borders of the components illustrated in the figures (for example, boxes, groups of boxes, of other shapes) are only meant to show an illustrative example of the boundaries. One component in some examples could be implemented as multiple components and multiple components could be implemented as a single component in some examples. Also, in some examples, a component that is represented to be an internal component of a component could be implemented as an external component of another component and vice versa in some examples. The components are not necessarily drawn to scale.
[0008] Various embodiments will be described hereinafter in conjunction with the appended drawings that are provided to illustrate and not limit the claimed invention as claimed, in which the same reference numerals refer to the same components, and in which:
[0009] FIG. 1 is a schematic block diagram illustrating an intelligent explainable artificial intelligence-based system for early cardiovascular disease detection from electrocardiogram (ECG) signals using evolutionary deep learning optimization, in accordance with an embodiment of the present invention.
[0010] FIG. 2 is a detailed architectural diagram illustrating functional modules including ECG acquisition, adaptive bio-signal fusion, evolutionary deep learning optimization, explainable AI processing, temporal disease prediction, and clinical decision support, in accordance with an embodiment of the present invention.
[0011] FIG. 3 is a flowchart (300) illustrating a method for early cardiovascular disease detection using an intelligent explainable AI framework with evolutionary deep learning optimization, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION
[0012] The current disclosure tackles the shortcomings of traditional ECG-based systems for detecting cardiovascular disease that depend on static, non-adaptive models and are hard to understand, which makes them less reliable for diagnosis and less likely to be accepted in clinical settings. The system gets around these problems by using an intelligent explainable artificial intelligence framework that can give outputs that are clinically useful and linked to ECG waveform components. The system also has an evolutionary deep learning optimization engine that changes model architectures and hyperparameters in real time to fit different types of patient data and signal conditions. The system also improves the accuracy of diagnoses by using adaptive bio-signal fusion to combine ECG signals with other physiological parameters. This makes the system more resistant to noise and variability. The system also protects privacy during distributed learning through federated learning mechanisms and allows for real-time processing with edge or neuromorphic computing units. The system also gives predictive information about how a disease will progress, not just how to find it. This makes it a complete and technically advanced solution to the problems with traditional methods.
[0013] The main goal of this disclosure is to present a system and method for early detection of cardiovascular disease from electrocardiogram (ECG) signals using an intelligent, explainable, and adaptive artificial intelligence framework. The goal of this disclosure is to create an evolutionary deep learning optimization mechanism that can dynamically find the best neural network architectures and improve diagnostic performance across different types of datasets. The goal of the system is to use explainable AI techniques to create outputs that can be understood by doctors and other healthcare professionals, which will make them more trustworthy and easier to use. The current disclosure also aims to include adaptive multi-modal physiological signal fusion to make detection more accurate and reliable in different situations. The current disclosure also seeks to facilitate real-time, low-power inference via edge or neuromorphic computing, while ensuring secure, privacy-preserving model training through federated learning methodologies. The invention also aims to provide predictive analysis of the progression of cardiovascular disease, which would make it easier to intervene early and improve patient outcomes.
[0014] The present invention presents an intelligent, explainable AI-based framework for the early detection of cardiovascular disease, incorporating various advanced technological elements to enhance diagnostic efficacy and interpretability. The invention uniquely combines an evolutionary deep learning optimization engine that uses genetic algorithms to search for dynamic neural architecture with an adaptive bio-signal fusion mechanism that combines ECG data with other physiological signals to make the system more reliable and accurate. A key innovative feature is the addition of an explainable AI module that turns model predictions into clinically useful interpretations that are linked to ECG waveform segments. This closes the gap between automated analysis and medical decision-making. The invention also adds the ability to predict how a disease will progress over time using advanced time-series and graph-based modeling techniques. This makes it possible for healthcare providers to take action before the disease gets worse. The system also uses federated learning to train models in a decentralized way that protects privacy, and it uses neuromorphic or edge computing to deploy models in real time while using less energy. These features work together to make a technically advanced, scalable, and compliant solution that is much better than current ECG-based diagnostic systems and has a strong inventive step that can be protected by a patent.
[0015] FIG. 1 shows a block diagram of an intelligent explainable artificial intelligence-based system (100) that uses evolutionary deep learning optimization to find early signs of heart disease in electrocardiogram (ECG) signals. This is an example of the present invention. The system (100) has a processor and memory architecture that connects all of its parts. These parts are: an ECG acquisition and preprocessing module (102), an adaptive bio-signal fusion module (104), an evolutionary deep learning optimization engine (106), an explainable artificial intelligence module (108), a temporal disease progression prediction module (110), a federated learning and privacy module (112), a neuromorphic or edge processing unit (114), a clinical decision support and alert module (116), a regulatory compliance and audit module (118), and a self-learning feedback optimization module (120).
[0016] The ECG acquisition and preprocessing module (102) is set up to get raw ECG signals from one or more sensing devices and do signal conditioning tasks like filtering out noise, correcting the baseline, and breaking the signals down into cardiac cycles. The adaptive bio-signal fusion module (104) then receives the processed signals. It uses an attention-based fusion mechanism to combine ECG data with other physiological parameters like photoplethysmography (PPG), blood pressure variability, and oxygen saturation. This module (104) makes signals more reliable and gives a full picture of the body's physiology for later analysis.
[0017] The evolutionary deep learning optimization engine (106) gets the combined data and uses genetic algorithms and neural architecture search techniques to dynamically create optimized neural network architectures. The optimized model is then used to find and classify heart problems correctly. The explainable artificial intelligence module (108) takes the model outputs and makes them easier to understand by connecting predictions to clinically important ECG waveform components like P waves, QRS complexes, and T waves. The temporal disease progression prediction module (110) uses time-series modeling and graph-based analytical techniques to look at both past and present data to figure out what the future risks are for the heart.
[0018] The federated learning and privacy module (112) allows for decentralized and secure model training across multiple data sources without sharing private patient information, which is necessary for privacy compliance. The neuromorphic or edge processing unit (114) makes it possible for wearable or remote healthcare devices to make inferences in real time and with low power. The clinical decision support and alert module (116) makes risk scores, alerts, and diagnostic reports for doctors. The regulatory compliance and audit module (118) keeps trackable logs and makes sure that medical and legal standards are followed. The self-learning feedback optimization module (120) uses feedback from clinicians and adaptive learning methods to constantly improve the system's performance, making it more accurate and stable over time.
[0019] In operation, all parts of the system (100) work together in an integrated way to achieve the new and inventive aspects of the present invention. The ECG acquisition and preprocessing module (102) captures and refines physiological signals, the adaptive bio-signal fusion module (104) improves data representation through multi-modal integration, and the evolutionary deep learning optimization engine (106) dynamically develops optimized predictive models that are tailored to different signal conditions. The explainable artificial intelligence module (108) makes predictions easier to understand, which builds trust in the clinic. The temporal disease progression prediction module (110) lets you look ahead and assess risk. The federated learning and privacy module (112) makes sure that model training is safe and spread out, and the neuromorphic or edge processing unit (114) makes it possible to deploy models in real time and with little energy use. The clinical decision support and alert module (116) also gives healthcare professionals useful information, and the regulatory compliance and audit module (118) makes sure that everything is legal and can be traced back. The self-learning feedback optimization module (120) constantly improves the system's performance, making it possible for an intelligent, adaptive, explainable, and privacy-preserving cardiovascular disease detection system that is much better than traditional methods.
[0020] FIG. 2 shows a detailed architectural diagram of functional modules (200) that are part of the intelligent explainable artificial intelligence framework for early cardiovascular disease detection from electrocardiogram (ECG) signals, according to one version of the present invention. The architecture (200) has a high-speed data processing pipeline that connects all of its parts: a signal input interface (202), a preprocessing and feature extraction unit (204), an adaptive bio-signal fusion engine (206), an evolutionary model optimization module (208), an explainable AI reasoning unit (210), a temporal prediction and risk analysis module (212), a federated learning coordination unit (214), an edge inference module (216), and a clinical output and visualization interface (218).
[0021] The signal input interface (202) is set up to get ECG signals from a number of different sources, such as wearable sensors, clinical devices, and remote monitoring systems. The preprocessing and feature extraction unit (204) gets the signals and does noise reduction, normalization, segmentation, and extraction of important features like heart rate variability, waveform morphology, and frequency-domain characteristics. The adaptive bio-signal fusion engine (206) then gets the processed data. It uses attention-based and context-aware fusion methods to combine ECG data with other physiological parameters to make a complete multi-dimensional data representation.
[0022] The evolutionary model optimization module (208) uses genetic algorithms, neural architecture search, and adaptive hyperparameter tuning to process the fused data and create optimized deep learning models that are specific to ECG classification and anomaly detection. The optimized model outputs are then sent to the explainable AI reasoning unit (210). This unit makes understandable explanations by linking predictions to specific ECG waveform segments and clinically important indicators. The temporal prediction and risk analysis module (212) uses time-series analysis and graph-based modeling to look at sequential and historical data in more depth in order to predict future cardiovascular risks and patterns of disease progression.
[0023] The federated learning coordination unit (214) makes it possible to train models in a way that keeps patient data private and distributed by gathering model updates from many decentralized sources without sharing the raw patient data. The edge inference module (216) enables real-time processing and rapid decision-making through the use of optimized lightweight or neuromorphic models installed on edge devices. The clinical output and visualization interface (218) shows doctors diagnostic results, risk scores, alerts, and interpretability visualizations on easy-to-use dashboards. This makes it easier for doctors to make informed medical decisions and keep an eye on patients.
[0024] In operation, all parts of the architecture (200) work together to achieve the new and creative parts of the present invention. The signal input interface (202) gets physiological data from multiple sources, and the preprocessing and feature extraction unit (204) changes the data into useful representations. The adaptive bio-signal fusion engine (206) makes the system more stable by combining inputs from different modes. The evolutionary model optimization module (208) creates optimized deep learning architectures on the fly to improve detection accuracy. The explainable AI reasoning unit (210) makes sure that outputs can be understood, which connects artificial intelligence to clinical practice. The temporal prediction and risk analysis module (212) lets doctors look ahead at how diseases will progress. The federated learning coordination unit (214) makes sure that distributed learning is done in a way that protects privacy, and the edge inference module (216) makes it possible to deploy in real time and with less energy. Lastly, the clinical output and visualization interface (218) gives you useful information, which together make for a technically advanced, adaptable, explainable, and scalable cardiovascular disease detection system that is much better than older systems.
[0025] An exemplary operation reveals a system for early cardiovascular disease detection from electrocardiogram (ECG) signals, utilizing an intelligent explainable artificial intelligence framework with evolutionary deep learning optimization. The system has two parts: an ECG acquisition and preprocessing module that gets and processes physiological signals, and an adaptive bio-signal fusion module that combines ECG data with other physiological parameters to make the data more useful. The system also has an evolutionary deep learning optimization engine that uses genetic algorithms and neural architecture search techniques to make and improve neural network architectures on the fly. In one version, the system also has an explainable artificial intelligence module that connects predictive outputs to clinically important ECG waveform components. In one version, the system also has a temporal disease progression prediction module that looks at sequential data to figure out cardiovascular risks. In one version, the system also has a federated learning module that lets models be trained in a way that is decentralized and keeps privacy. In one version, the system also has a neuromorphic or edge processing unit that can do real-time, low-power inference. In one version, the system also has a clinical decision support module that can send alerts, diagnostic reports, and actionable insights. This makes it possible to accurately, adaptively, and understandably detect cardiovascular disease.
[0026] In one embodiment, the processor is set up to get ECG signals and do preprocessing tasks like noise filtering, normalization, and segmentation to find useful cardiac features. In one version, the processor is set up to run an adaptive bio-signal fusion algorithm that uses attention-based mechanisms to combine ECG signals with other physiological data. In one version, the processor is set up to run an evolutionary deep learning optimization process that uses genetic algorithms and neural architecture search to find the best model structures and hyperparameters. In one version, the processor is set up to use the optimized model to look at processed data and find cardiovascular problems, and then make diagnostic outputs. In one version, the processor is set up to run an explainable artificial intelligence module that makes sense of ECG waveform components and gives interpretable insights. In one version, the processor is set up to use time-series and graph-based analytical models to predict risk over time. In one version, the processor is set up to allow federated learning by combining decentralized model updates while keeping data private. In one version, the processor is set up to support real-time inference using edge or neuromorphic processing. In one version, the processor is also set up to send alerts, keep audit logs, and use feedback-driven learning mechanisms to keep track of model performance.
[0027] In another embodiment of the invention, there is a wearable-integrated cardiovascular monitoring system in which the ECG acquisition module is built into a small, wearable device that is always recording real-time heart signals. The system also has edge-based evolutionary learning features that let it change model parameters based on the user's own physiological patterns. The explainable AI module is set up to give real-time visual feedback through a mobile app interface, pointing out unusual waveform segments and giving personalized health insights. The system also uses cloud-assisted federated learning to regularly sync model updates between different devices. This improves the performance of the global model while keeping user privacy. This version makes it easier to access and allows for continuous, personalized heart monitoring outside of traditional clinical settings.
[0028] In yet another embodiment, the present invention is a clinical decision support platform that is built into hospital information systems. The system gets ECG data from many clinical sources and processes it all in one place using high-performance computing infrastructure. The evolutionary optimization engine is set up to change models based on data from the whole population, which makes diagnoses more accurate across a wide range of groups. The system also has a regulatory compliance module that makes reports that are easy to understand and can be used for medical-legal documentation and audits. The temporal prediction module also uses predictive analytics to give long-term risk stratification and treatment recommendations. This helps doctors make smart choices and improves patient outcomes in critical care settings.
[0029] Let's look at a real-life example to show how the current disclosure works. Think of a patient who wears an ECG monitoring device that constantly picks up heart signals while they go about their daily life. The system processes the acquired ECG data and other physiological parameters, such as heart rate variability and oxygen saturation. The system processes the signals ahead of time and uses adaptive bio-signal fusion to create a full physiological profile. The evolutionary deep learning optimization engine automatically chooses the best model to look at the data and find early signs of heart problems. The explainable AI module gives you insights that you can understand by pointing out specific parts of the ECG waveform that are related to the condition that was found. At the same time, the temporal prediction module looks at past data to figure out how likely it is that a person's heart will be at risk in the future. The system sends out real-time alerts and diagnostic reports that both the patient and healthcare professionals can see. This lets them intervene early and keep an eye on the patient at all times while keeping their data private through federated learning mechanisms.
[0030] FIG. 3 is a flowchart (300) that shows how to use an intelligent explainable artificial intelligence framework with evolutionary deep learning optimization to find cardiovascular disease early from electrocardiogram (ECG) signals, as described in this embodiment. The process starts with a Start step (302) and then moves on to a step (304).
[0031] At step (304), one or more sensing devices, such as wearable sensors, clinical instruments, or remote monitoring systems, collect ECG signals and, if desired, physiological data.
[0032] In step (306), the method uses noise filtering, normalization, and segmentation techniques to preprocess the signals that were gathered. This is done to get cardiac cycles and other important features.
[0033] At step (308), the method uses attention-based fusion mechanisms to combine ECG data with other physiological parameters to create a complete data representation.
[0034] The next step (310) is an evolutionary deep learning optimization process that uses genetic algorithms and neural architecture search to find the best model architecture and parameters on the fly.
[0035] The optimized model is used to look at the combined data and find heart problems at step (312). The method then moves on to step (314), where an explainable AI module creates outputs that can be understood by linking predictions to clinically relevant ECG waveform components.
[0036] In step (316), time-series and graph-based modeling techniques are used to do a temporal analysis that predicts future cardiovascular risk and disease progression.
[0037] The method then moves on to step (318), where federated learning is used to update model parameters across multiple data sources while keeping the data private.
[0038] At step (320), edge or neuromorphic processing makes real-time inference possible for deployment with low latency.
[0039] The method then goes to step (322), where it makes and shows users clinical decision support outputs like alerts, risk scores, and diagnostic reports.
[0040] Finally, at step (324), feedback-driven learning is used to keep improving the system's performance. The method ends at the End step.
[0041] The current disclosure presents numerous technical benefits compared to traditional ECG-based cardiovascular disease detection systems. First, it combines multiple sensors and adaptive bio-signal fusion to combine ECG signals with other physiological parameters. This makes the signals stronger and more accurate for diagnosis in different situations. This improved multi-modal analysis makes noise sensitivity and false predictions much less likely. Also, using an evolutionary deep learning optimization engine lets neural network architectures be generated and changed on the fly, which makes the model work better with a wide range of patient datasets. Adding an explainable artificial intelligence module gives clinically interpretable outputs that are linked to ECG waveform components. This makes healthcare professionals more likely to trust and use the technology. Also, being able to predict how a disease will progress over time allows for proactive risk assessment instead of reactive diagnosis. Federated learning makes it possible to train models in a way that protects privacy, and neuromorphic or edge processing makes it possible to deploy models in real time and with low power in wearable and remote healthcare settings. All of these technical improvements work together to make a solution that is scalable, adaptable, and easy to understand, and it works much better than traditional systems.
[0042] The present disclosure offers a definitive and pragmatic resolution to a substantial technical challenge in the domain of cardiovascular disease detection via electrocardiogram (ECG) signals, addressing the shortcomings of traditional systems characterized by poor interpretability, restricted adaptability, and inadequate integration of multi-modal physiological data. This disclosure describes some of the technical features and functions it offers, such as an adaptive bio-signal fusion mechanism for combining multiple physiological inputs, an evolutionary deep learning optimization engine for creating dynamic model architectures, and an explainable artificial intelligence module for producing diagnostic outputs that can be understood by clinicians. The system also has the ability to predict how a disease will progress over time, federated learning to protect privacy during distributed training, and edge or neuromorphic processing to make deployment quick and energy-efficient. These integrated parts work together to accurately, reliably, and scalably find cardiovascular disease. This solves the problems with traditional methods and provides a technically advanced and practically usable solution in line with the current invention.
, Claims:CLAIMS
We Claim:
1. A system (100) for early cardiovascular disease detection from electrocardiogram (ECG) signals using an intelligent explainable artificial intelligence framework with evolutionary deep learning optimization, the system comprising:
a) one or more sensors configured to acquire ECG signals and optional physiological parameters;
a communication interface;
b) a memory storing executable instructions;
c) a processor operatively coupled to the memory and the communication interface,
wherein the processor is configured to:
a) preprocess the acquired ECG signals using noise filtering, normalization, and segmentation techniques;
b) perform adaptive bio-signal fusion by integrating ECG signals with additional physiological parameters to generate a unified data representation;
c) execute an evolutionary deep learning optimization process comprising genetic algorithms and neural architecture search to dynamically determine an optimized model architecture;
d) analyze the unified data representation using the optimized model to detect cardiovascular abnormalities;
e) generate interpretable diagnostic outputs using an explainable artificial intelligence module mapped to ECG waveform components.

2. The system (100) as claimed in claim 1, wherein the adaptive bio-signal fusion comprises an attention-based mechanism configured to dynamically assign weights to ECG and auxiliary physiological signals based on signal reliability and contextual relevance.

3. The system (100) as claimed in claim 1, wherein the processor is further configured to perform temporal disease progression prediction using time-series modeling and graph-based analysis to estimate future cardiovascular risk.

4. The system (100) as claimed in claim 1, wherein the processor is further configured to enable federated learning by aggregating model updates from multiple decentralized sources without sharing raw patient data, thereby preserving data privacy.

5. The system (100) as claimed in claim 1, further comprising an edge or neuromorphic processing unit configured to perform real-time, low-power inference, and a clinical decision support module configured to generate alerts, risk scores, and diagnostic reports for end users.

6. A method for early cardiovascular disease detection from electrocardiogram (ECG) signals using an intelligent explainable artificial intelligence framework with evolutionary deep learning optimization, the method comprising:
a) acquiring ECG signals and optional physiological parameters from one or more sensing devices;
preprocessing the acquired signals using noise filtering, normalization, and segmentation;
b) performing adaptive bio-signal fusion to generate a unified physiological data representation;
executing an evolutionary deep learning optimization process to dynamically determine an optimized model architecture;
c) analyzing the unified data representation using the optimized model to detect cardiovascular abnormalities;
d) generating interpretable diagnostic outputs using an explainable artificial intelligence module.

7. The method as claimed in claim 6, wherein performing adaptive bio-signal fusion comprises applying an attention-based mechanism to combine ECG signals with additional physiological data based on signal quality and contextual importance.

8. The method as claimed in claim 6, further comprising predicting cardiovascular disease progression using temporal analysis of historical and real-time data through time-series and graph-based modeling techniques.

9. The method as claimed in claim 6, further comprising performing federated learning by aggregating model updates from multiple distributed nodes while maintaining data privacy and security.

10. The method as claimed in claim 6, further comprising generating real-time alerts, risk scores, and clinician-readable diagnostic reports, and continuously updating model parameters using feedback-driven learning mechanisms.