ECG Delineation via Convolutional Neural Networks
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Solution Overview
Problem
Current methods for cardiac signal analysis, particularly electrocardiogram (ECG) interpretation, face challenges in accurately and efficiently identifying wave patterns and abnormalities without specialized expertise, as they often require beat-by-beat processing, feature extraction, and are limited in handling multiple leads and hidden P waves, leading to instability and suboptimal performance.
Innovation Solution
The use of convolutional neural networks for simultaneous delineation and multi-label classification of cardiac signals, which processes signals of any duration and identifies various wave types and abnormalities in a single step, providing a fixed format output that includes the presence and characterization of waves and labels like 'Atrial fibrillations' or 'Normal sinus rhythm', without relying on handcrafted features or preliminary beat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If prior art automated cardiac signal 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 combines two previously separate processing streams into a unified neural network architecture: (1) a delineation network that identifies wave onsets and offsets, and (2) a classification network that detects abnormalities. These networks share common convolutional feature extraction layers, allowing simultaneous optimization of both localization precision and classification accuracy, thereby achieving high-speed automated interpretation without sacrificing reliability
Solution Approach 2:
The patent introduces an intermediate representation layer where raw ECG signals are transformed into a standardized feature space through shared convolutional layers. This intermediate representation serves both the delineation task (precise wave boundary detection) and the classification task (abnormality detection), enabling the system to maintain high interpretation quality while operating at automated speeds
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 self-service automated interpretation through neural networks that autonomously perform both wave delineation and abnormality classification without requiring human cardiologist intervention for routine cases. The system independently processes ECG signals, identifies waves, detects abnormalities, and generates diagnostic reports, thereby achieving expert-level interpretation quality while eliminating the time delays and costs associated with manual telecardiology review
3Ease of operation
If multiscale wavelet analysis is used for delineation, then wave detection is performed, but the computation is unstable due to threshold use and it cannot handle multiple or hidden P waves
Solution Approach 1:
The patent replaces the mechanical threshold-based wavelet analysis system with a neural network-based delineation system. Instead of applying fixed thresholds to wavelet coefficients, the neural network learns optimal detection boundaries from training data, automatically adapting to various ECG morphologies. This substitution eliminates the instability caused by arbitrary threshold selection and enables reliable detection of challenging wave patterns including multiple P waves and hidden P waves embedded within other complexes
4Ease of operation
If Hidden Markov Models are used for wave recognition, then wave identification is achieved, but the feature design is cumbersome and the Gaussian model is not well adapted
Solution Approach 1:
The patent extracts and removes the cumbersome manual feature design step from the wave recognition process. Instead of requiring experts to handcraft features and fit Gaussian models, the neural network automatically learns optimal features directly from raw ECG signals through its convolutional layers. This extraction of the feature engineering step simplifies the overall system while improving adaptability to diverse ECG patterns
5Productivity
If standard classification algorithms are used for wave characterization, then beat classification is performed, but the handcrafted features are suboptimal and may have erased crucial information
Solution Approach 1:
The patent performs preliminary action by having the delineation network precisely identify wave boundaries and extract clean waveforms before passing them to the classification network. This preliminary delineation step ensures that the classification algorithm receives high-quality, precisely delimited wave data rather than relying on potentially information-lossy handcrafted features extracted earlier in the pipeline, thereby improving characterization accuracy while maintaining efficient processing
Data Source
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AI summary
The invention relates to a method for computerizing delineation and/or multi-label classification of an ECG signal, comprising: applying a neural network to said ECG. labelling the ECG. and optionally displaying the labels according to time with the ECG signal.