Optimal Multi-Stage Arrhythmia Classification Approach

Authors

Jianwei Zhang, Chapman University
Huimin Chu, Zhejiang University
Daniele Struppa, Chapman UniversityFollow
Jianming Zhang, Shaoxing People's Hospital
Sir Magdi Yacoub, Imperial College London
Hesham el-Askary, Chapman UniversityFollow
Anthony Chang, CHOC Children’s Hospital
Louis Ehwerhemuepha, Chapman University
Islam Abudayyeh, Loma Linda University
Alexander Barrett, Chapman University
Guohua Fu, Zhejiang University
Hai Yao, Zhejiang Cachet Jetboom Medical Devices CO.LTD
Dongbo Li, Zhejiang University
Hangyuan Guo, Shaoxing People's Hospital
Cyril Rakovski, Chapman UniversityFollow

Document Type

Article

Publication Date

2-19-2020

Abstract

Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F₁-Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F₁-Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F₁-Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources.

Comments

This article was originally published in Scientific Reports, volume 10, in 2020. https://doi.org/10.1038/s41598-020-59821-7

Recommended Citation

Zheng, J., Chu, H., Struppa, D. et al. Optimal Multi-Stage Arrhythmia Classification Approach. Sci Rep 10, 2898 (2020). https://doi.org/10.1038/s41598-020-59821-7

Peer Reviewed

1

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.