ECG pattern classification by feature searching algorithm based on maximal divergence.
- Author:
Yuzhen CAO
1
;
Zengfei FAN
Author Information
1. College of Precision Instrument & Opto-electmrnics Engineering, Tianjin University, Tianjin 300072, China. yzcao@tju.edu.cn
- Publication Type:Journal Article
- MeSH:
Algorithms;
Bundle-Branch Block;
classification;
physiopathology;
Electrocardiography;
methods;
Humans;
Neural Networks (Computer);
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2008;25(1):53-56
- CountryChina
- Language:Chinese
-
Abstract:
This paper presents a method of using feature searching algorithm based on maximal divergence value to get the optimized feature combinations at different dimensions from feature space. Feature space is obtained through wavelet transform on ECG beat. Then the feature vector is determined by analyzing the changes of divergence value of those optimized feature combinations along with the dimensions. BP artificial neural network is trained by the feature vector and four types of ECG beats(normal beat, left bundle branch block beat, right bundle branch block beat and paced beat) obtained from MIT-BIH database are classified with a success of 93.9%.
