An interpretable machine learning method for heart beat classification
- VernacularTitle:一种用于心拍分类的可解释机器学习方法
- Author:
Jinbao ZHANG
1
;
Peiyu HE
1
;
Pian TIAN
1
;
Jianmin CAI
1
;
Fan PAN
1
;
Yongjun QIAN
2
;
Qijun ZHAO
3
Author Information
1. School of Electronic Information, Sichuan University, Chengdu, 610065, P. R. China
2. Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China
3. School of Computer Science (School of Software), Sichuan University, Chengdu, 610065, P. R. China
- Publication Type:Journal Article
- Keywords:
Machine learning;
Tsetlin Machine;
beat classification;
interpretability;
artificial intelligence
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2023;30(02):185-190
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the application of Tsetlin Machine (TM) in heart beat classification. Methods TM was used to classify the normal beats, premature ventricular contraction (PVC) and supraventricular premature beats (SPB) in the 2020 data set of China Physiological Signal Challenge. This data set consisted of the single-lead electro-cardiogram data of 10 patients with arrhythmia. One patient with atrial fibrillation was excluded, and finally data of the other 9 patients were included in this study. The classification results were then analyzed. Results The classification results showed that the average recognition accuracy of TM was 84.3%, and the basis of classification could be shown by the bit pattern interpretation diagram. Conclusion TM can explain the classification results when classifying heart beats. The reasonable interpretation of classification results can increase the reliability of the model and facilitate people's review and understanding.
