Adaptive learning-based method for classification of arrhythmic heartbeats(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2019年第1期
- Page:
- 92-96
- Research Field:
- 医学信号处理与医学仪器
- Publishing date:
Info
- Title:
- Adaptive learning-based method for classification of arrhythmic heartbeats
- Author(s):
- WANG Kai1; YANG Shu1; 2
- 1. Department of Health Management, Bengbu Medical College, Bengbu 233030, China; 2. School of Information and Computer, Hefei University of Technology, Hefei 233009, China
- Keywords:
- ; Keywords: electrocardiogram; adaptive classifier; feature extraction; classification
- PACS:
- R318；TP391
- DOI:
- DOI:10.3969/j.issn.1005-202X.2019.01.018
- Abstract:
- Abstract: Arrhythmia is a common electrocardiogram (ECG) abnormality in heart diseases, among which premature ventricular contraction (PVC) is a widespread arrhythmia caused by ectopic heartbeat. The detection of PVC by ECG signals is significant for predicting possible heart failure. Herein an ECG signal classification algorithm for PVC heartbeat classification is proposed. The feature extraction model of PVC abnormal heartbeat classification based on adaptive learning is mainly studied. The posterior probability of heartbeat correlation is calculated and then combined with information classifier to improve the classification performance. The ECG data adopted in this study are from MIT-BIH arrhythmia database. The research results show that the proposed algorithm can effectively improve the accuracy of adaptive classifier for small-sample heart beats of nonlinear manifold data.
Last Update: 2019-01-25