ECG Risk Prediction Model Using Self-Supervised Deep Learning
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Solution Overview
Problem
Conventional electrocardiogram analysis methods rely on statistical analysis, which struggles to capture nonlinear and complex patterns, leading to inaccurate risk stratification and prediction of heart diseases like arrhythmia, particularly atrial fibrillation, due to signal interference and noise.
Innovation Solution
A deep learning model is developed to process electrocardiogram data, utilizing a self-supervised learning approach with masking-based embedding and clustering, combined with HRV feature extraction and ensemble learning, to predict heart disease risk and stratify patient's risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If statistical analysis methods are used for ECG data processing, then the analysis process is simple and easy to implement, but the accuracy of heart disease prediction and risk stratification is insufficient due to inability to capture nonlinear patterns
Solution Approach 1:
The patent replaces statistical analysis methods with deep learning neural network models that can capture nonlinear patterns in ECG data. The neural network architecture including convolutional layers, pooling layers, and fully connected layers enables the system to learn complex temporal dependencies and spatial relationships in multi-lead ECG signals, significantly improving prediction accuracy while maintaining operational simplicity through automated feature extraction.
2Device complexity
If statistical-based models are used for ECG data processing, then the model structure is simple, but the performance degrades due to signal overlaps and noise from muscle movement, respiration, and electrical interference
Solution Approach 1:
The patent segments the ECG signal processing into multiple independent components: noise filtering module, feature extraction module, and classification module. The noise filtering module separately processes each ECG lead through bandpass filters to remove power line interference and muscle artifacts, then the cleaned signals are fed to the neural network for pattern recognition, improving reliability without excessive complexity.
Solution Approach 2:
The patent introduces an intermediary feature extraction layer that transforms raw ECG signals into meaningful representations. The neural network learns optimal feature representations from the filtered signals, creating a buffer between the noisy input and the final prediction, thereby improving reliability by separating signal processing from classification functions.
3Measurement precision
If deep learning models are used for ECG data processing, then the accuracy of heart disease prediction is improved by capturing complex patterns, but the computational requirements and model complexity increase
Solution Approach 1:
The patent applies partial action by using a carefully designed neural network architecture that processes only the most informative features from each ECG lead. The model uses convolutional layers with appropriate kernel sizes to extract local patterns, pooling layers to reduce dimensionality, and a limited number of fully connected layers for classification, achieving high accuracy without excessive computational complexity.
Data Source
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AI summary
Proposed is a method of generating a model for predicting heart disease according to various exemplary embodiments of the present disclosure. The method includes obtaining a plurality of electrocardiogram data and generating the model for predicting heart disease on the basis of the plurality of electrocardiogram data. The generating of the model includes constructing a learning data set on the basis of the electrocardiogram data, and generating a model for predicting a risk of heart disease by performing training on one or more network functions on the basis of the learning data set, wherein the learning data set includes a first learning data set and a second learning data set which are classified for different training purposes, and the model for predicting the risk of heart disease stratifies and provides prediction information on the risk of future heart disease on the basis of a patient's electrocardiogram data.