ECG Wave Classification Using Deep Learning Segmentation
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Solution Overview
Problem
Conventional ECG analysis systems face challenges in accurately and quickly detecting and classifying arrhythmia due to their reliance on rule-based algorithms, which are less accurate and time-consuming, especially in real-time monitoring scenarios where noise detection is critical.
Innovation Solution
An ECG noise discriminating apparatus that employs a deep learning-based segmentation algorithm to classify ECG waves into P, Q, R, S, T waves, and noise, allowing for quick classification of heartbeats and removal of noise to facilitate fast and accurate medical decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based algorithms are used for ECG analysis, then the system is simple to implement, but the accuracy of arrhythmia detection is low
Solution Approach 1:
The patent replaces rule-based algorithms with a deep learning-based automatic reading algorithm. The system uses a neural network model trained on ECG data to automatically detect and classify arrhythmias, substituting the mechanical rule-based approach with an intelligent system that learns patterns from data, thereby improving detection accuracy while maintaining operational simplicity.
Solution Approach 2:
The patent transforms the ECG signal processing approach by changing from fixed rule-based parameters to dynamic parameters learned from data. The system adjusts detection thresholds, wave identification criteria, and classification parameters based on learned patterns from training data, enabling adaptive and accurate arrhythmia detection across diverse ECG variations.
2Productivity
If conventional ECG reading systems are used, then the system structure is simple, but the reading time is too long for real-time monitoring
Solution Approach 1:
The patent replaces manual and rule-based ECG reading processes with an automatic deep learning-based reading system. The neural network processes ECG signals in real-time, automatically identifying waves, calculating intervals, and detecting arrhythmias without manual intervention, thereby dramatically increasing reading speed and enabling real-time monitoring applications.
Solution Approach 2:
The patent implements preliminary training of the deep learning model on extensive ECG data before deployment. The system pre-learns patterns of normal and abnormal ECG waves, allowing it to perform rapid accurate readings in real-time without requiring complex processing during actual monitoring, thus reducing time loss while maintaining simplicity.
3Measurement precision
If noise is not accurately detected and removed, then the system operation is simple, but the reading accuracy decreases
Solution Approach 1:
The patent merges noise detection and removal functions into the unified deep learning-based automatic reading system. The neural network simultaneously performs wave detection, noise identification, and signal cleaning in an integrated process, eliminating the need for separate noise processing steps and maintaining system simplicity while improving reading accuracy through comprehensive signal analysis.
Data Source
AI summary
Disclosed is an apparatus for electrocardiogram (ECG) wave classification using machine learning being capable of applying a segmentation technique to an ECG wave, checking a feature for each section of a P wave, a Q wave, an R wave, an S wave, a T wave, and a noise wave included in the ECG wave, quickly classifying heart beats into a normal beat (N), a supraventricular beat(S), a ventricular beat (V), and noise, and removing the noise to make medical decisions quickly and accurately.