Arrhythmia Identification via Multi-Domain ECG Feature Extraction
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Solution Overview
Problem
Current methods for identifying arrhythmia using electrocardiogram (ECG) signals are limited in accuracy and efficiency, particularly in distinguishing between various types of arrhythmias, due to the complexity of ECG waveforms and noise interference.
Innovation Solution
A method involving the acquisition of ECG signals, preprocessing to denoise, detection of feature wave information, extraction of feature parameters, normalization, and classification using a neural network, random forest, or support vector machine to identify specific arrhythmias, such as atrial fibrillation, normal, or bundle branch block heartbeats, by utilizing time, wavelet, and frequency domain features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ECG analysis methods are used, then the system is simple and easy to implement, but the accuracy of arrhythmia identification is limited
Solution Approach 1:
The patent segments the complex ECG analysis task into multiple distinct feature extraction modules: time domain features (HRV parameters), frequency domain features (spectral analysis), and nonlinear dynamics features (entropy measures). Each module processes specific aspects of the ECG signal independently, then their results are integrated by a classifier to achieve high identification accuracy while maintaining manageable system complexity through modular design.
2Measurement precision
If multiple feature extraction methods are applied, then the identification accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The patent performs preliminary action by pre-defining and pre-processing multiple feature extraction pipelines before actual arrhythmia detection. The system pre-computes time domain, frequency domain, and nonlinear features from the ECG signal, then stores these extracted features for rapid classification. This preliminary feature extraction and storage enables fast real-time identification without repeated heavy computations during the actual detection phase.
3Measurement precision
If comprehensive feature extraction is performed, then the classification accuracy improves, but the computational resources required increase
Solution Approach 1:
The patent extracts and isolates the most discriminative features from the comprehensive set of ECG characteristics. Instead of using all available features, the system selectively extracts key time domain features (such as HRV parameters), frequency domain features (spectral power distribution), and nonlinear features (entropy measures) that are most relevant for arrhythmia classification. This selective extraction reduces computational energy consumption while maintaining high classification accuracy by focusing on the most informative features.
Data Source
AI summary
The present application discloses a method for identifying arrhythmia, a device for identifying arrhythmia, and a computer readable medium. The method includes: acquiring a type of arrhythmia to be identified; acquiring an ECG signal collected by an ECG acquisition device; detecting feature wave information in the ECG signal according to the type of arrhythmia to be identified; extracting a feature parameter from the denoised ECG signal and the feature wave information according to the type of arrhythmia to be identified; and identifying, by a classifier, an occurrence of the type of arrhythmia to be identified according to the feature parameter.