An interpretable machine learning method for heart beat classification_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHANG Jinbao ¹ , HE Peiyu ¹ , TIAN Pian ¹ , CAI Jianmin ¹ ,  PAN Fan ¹ ,  QIAN Yongjun ² , ZHAO Qijun ³

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;

Corresponding author：

PAN Fan, Email: panfan@scu.edu.cn; QIAN Yongjun, Email: qianyongjun@scu.edu.cn

Keywords：

Machine learning; Tsetlin Machine; beat classification; interpretability; artificial intelligence

DOI：

10.7507/1007-4848.202204067

Video：

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Abstract Full text Figures/Tables Video References Cited by

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 electrocardiogram 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.

Citation： ZHANG Jinbao, HE Peiyu, TIAN Pian, CAI Jianmin, PAN Fan, QIAN Yongjun, ZHAO Qijun. An interpretable machine learning method for heart beat classification. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2023, 30(2): 185-190. doi: 10.7507/1007-4848.202204067 Copy

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1. Kuriachan VP, Sumner GL, Mitchell LB. Sudden cardiac death. Curr Probl Cardiol, 2015, 40(4): 133-200.
2. 中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2019概要. 中国循环杂志, 2020, 35(9): 833-854.
3. 陈璇, 王雨锋, 张筑欣, 等. 中国心律失常现状及治疗进展. 中国研究型医院, 2020, 7(1): 75-78, 198-201.
4. 庞彦伟, 李潇, 梁金升, 等. 基于卷积神经网络的第一导联心电图心拍分类. 天津大学学报(自然科学与工程技术版), 2018, 51(10): 997-1004.
5. 朱彬如, 万相奎, 金志尧, 等. 运用双向长短期记忆模型的心拍分类算法. 华侨大学学报(自然科学版), 2021, 42(3): 384-390.
6. 张异凡, 黄亦翔, 汪开正, 等. 用于心律失常识别的LSTM和CNN并行组合模型. 哈尔滨工业大学学报, 2019, 51(10): 76-82.
7. 杨浩, 黄茂林, 蔡志鹏, 等. 融合CNN和BiLSTM的心律失常心拍分类模型. 中国生物医学工程学报, 2020, 39(6): 719-726.
8. Granmo OC. The Tsetlin machine—A game theoretic bandit driven approach to optimal pattern recognition with propositional logic. 2021, arXiv: 1804.01508v15.
9. Cai ZP, Liu CY, Gao HX, et al. Open-access long-term wearable ECG database for premature ventricular contractions and supraventricular premature beat detection. J Med Imaging Health Inform, 2020, 10(11): 2663-2667.
10. 任杰, 杨丽晓. 基于小波变换系数的心电信号基线漂移噪声去除方法. 医疗卫生装备, 2010, 31(11): 24-26.
11. Tsetlin ML. On behaviour of finite automata in random medium. Avtom I Telemekhanika, 1961, 22(10): 1345-1354.
12. von Neumann J, Morgenstern O. Theory of Games and Economic Behavior. Princeton: Princeton University Press, 2007.
13. Haugland V, Kjølleberg M, Larsen SE, et al. A two-armed bandit collective for hierarchical examplar based mining of frequent itemsets with applications to intrusion detection. In: Nguyen N (eds) Transactions on Computational Collective Intelligence XIV. Lecture Notes in Computer Science. Berlin: Springer, 2014.
14. Lecun Y, Bottou L. Gradient-based learning applied to document recognition. P IEEE, 1998, 86(11): 2278-2324.
15. 柳俊, 王莺. 明明白白心电图 (第四版). 广州: 广州科技出版社, 2013. 56-58.
16. Cai Z, Wang T, Shen Y, et al. Robust PVC identification by fusing expert system and deep learning. Biosensors (Basel), 2022, 12(4): 185.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

An interpretable machine learning method for heart beat classification

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