ECG Delineation Using Convolutional Neural Networks
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Solution Overview
Problem
Current ECG analysis methods are limited in their ability to accurately interpret ECG signals without specialized expertise, as they struggle with handling multiple leads, hidden waves, and multi-label classification, leading to inefficient and unreliable results.
Innovation Solution
The use of convolutional neural networks for ECG analysis, specifically fully convolutional and recurrent neural networks, which enable the processing of ECG signals of any duration, identify hidden waves, and provide multi-label classification directly, without the need for beat-by-beat processing or feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If prior art automated ECG interpretation softwares are used, then interpretation is delivered in seconds, but the quality of interpretation is low with frequent false alarms
Solution Approach 1:
The patent replaces traditional signal processing methods (mechanical systems based on wavelet analysis and Hidden Markov Models) with a neural network-based system. The neural network automatically learns features from raw ECG signals without requiring manual feature extraction or threshold-based detection, thereby improving interpretation quality while maintaining fast processing speeds.
Solution Approach 2:
The patent changes the fundamental parameters of ECG analysis by using a neural network that processes raw signals directly, rather than analyzing pre-processed features or wavelet coefficients. This parameter change enables the system to capture subtle patterns that traditional methods miss, reducing false alarms while maintaining high-speed interpretation.
2Reliability
If telecardiology centers are used for ECG interpretation, then interpretation quality is high, but the process is slow and expensive
Solution Approach 1:
The patent implements a self-service system where the neural network automatically performs ECG interpretation without requiring human cardiologist intervention for every case. The system independently analyzes ECG signals, detects anomalies, and provides diagnoses, enabling high-quality interpretation to be delivered instantly rather than requiring slow manual review by specialists.
Solution Approach 2:
The patent substitutes the manual mechanical process of cardiologist review with an automated neural network system. This replacement maintains high interpretation quality by learning from extensive training data while eliminating the time delay and cost associated with human expert review for every ECG.
3Ease of manufacture
If multiscale wavelet analysis is used for ECG delineation, then the method can handle single lead analysis, but it cannot deal with multiple P waves or hidden P waves and requires unstable thresholding
Solution Approach 1:
The patent changes the analysis parameters by using a neural network that processes the entire ECG signal simultaneously across all leads and time points, rather than analyzing single leads sequentially with fixed thresholds. This enables the system to detect multiple P waves and hidden P waves by learning their various temporal and spatial patterns without requiring unstable threshold adjustments.
Solution Approach 2:
The patent creates a universal neural network model that can handle multiple types of ECG patterns simultaneously - single and multiple P waves, hidden P waves, various QRS complexes, and T waves - all within a single unified framework. This multi-functional approach eliminates the need for separate analysis methods for different wave patterns while maintaining simplicity.
4Measurement precision
If Hidden Markov Models are used for ECG delineation, then the method can recognize waves based on learned features, but the feature design is cumbersome and the Gaussian model is not well adapted
Solution Approach 1:
The patent replaces the complex mechanical process of manual feature design and Gaussian model fitting with a neural network that automatically learns optimal features from raw ECG data. The neural network's distributed representation learning captures complex wave patterns without requiring explicit feature engineering or assumptions about signal distributions, thereby maintaining high recognition accuracy while dramatically reducing system complexity.
5Productivity
If rule-based algorithms using temporal and morphological indicators are used, then the algorithms can detect anomalies based on computed features, but they do not reflect the way cardiologists analyze ECGs and are crude simplifications
Solution Approach 1:
The patent changes the detection parameters by using a neural network that learns complex, non-linear relationships in ECG data rather than relying on simple temporal and morphological rules. The neural network captures subtle patterns and interactions that crude rule-based systems miss, thereby improving detection accuracy while maintaining full automation. The system learns directly from training data what cardiologists consider important, rather than imposing simplified rules.
Data Source
AI summary
A method for computerizing delineation and/or multi-label classification of an ECG signal, includes: applying a neural network to the ECG whereby labelling the ECG, and optionally displaying the labels according to time, optionally with the ECG signal.