Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Xiaoye Zhao; Jucheng Zhang; Yinglan Gong; Lihua Xu; Haipeng Liu; Shujun Wei; Yuan Wu; Ganhua Cha; Haicheng Wei; Jiandong Mao; Ling Xia

doi:10.3389/fphys.2022.854191

Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram

Front Physiol. 2022 May 30:13:854191. doi: 10.3389/fphys.2022.854191. eCollection 2022.

Authors

Xiaoye Zhao^{1

2

3}, Jucheng Zhang⁴, Yinglan Gong^{5

6}, Lihua Xu⁷, Haipeng Liu⁸, Shujun Wei⁹, Yuan Wu⁹, Ganhua Cha², Haicheng Wei², Jiandong Mao^{1

2

3}, Ling Xia¹⁰

Affiliations

¹ School of Instrument Science and Opto-Electronic Engineering, Hefei University of Technology, Hefei, China.
² School of Electrical and Information Engineering, North Minzu University, Yinchuan, China.
³ Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, Yinchuan, China.
⁴ Department of Clinical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
⁵ Hangzhou Maixin Technology Co., Ltd., Hangzhou, China.
⁶ Institute of Wenzhou, Zhejiang University, Wenzhou, China.
⁷ Hangzhou Linghua Biotech Ltd., Hangzhou, China.
⁸ Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom.
⁹ Department of Cardiology, Ningxia Hui Autonomous Region People's Hospital, Yinchuan, China.
¹⁰ Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou, China.

Abstract

Background: Myocardial ischemia is a common early symptom of cardiovascular disease (CVD). Reliable detection of myocardial ischemia using computer-aided analysis of electrocardiograms (ECG) provides an important reference for early diagnosis of CVD. The vectorcardiogram (VCG) could improve the performance of ECG-based myocardial ischemia detection by affording temporal-spatial characteristics related to myocardial ischemia and capturing subtle changes in ST-T segment in continuous cardiac cycles. We aim to investigate if the combination of ECG and VCG could improve the performance of machine learning algorithms in automatic myocardial ischemia detection. Methods: The ST-T segments of 20-second, 12-lead ECGs, and VCGs were extracted from 377 patients with myocardial ischemia and 52 healthy controls. Then, sample entropy (SampEn, of 12 ECG leads and of three VCG leads), spatial heterogeneity index (SHI, of VCG) and temporal heterogeneity index (THI, of VCG) are calculated. Using a grid search, four SampEn and two features are selected as input signal features for ECG-only and VCG-only models based on support vector machine (SVM), respectively. Similarly, three features (S _I , THI, and SHI, where S _I is the SampEn of lead I) are further selected for the ECG + VCG model. 5-fold cross validation was used to assess the performance of ECG-only, VCG-only, and ECG + VCG models. To fully evaluate the algorithmic generalization ability, the model with the best performance was selected and tested on a third independent dataset of 148 patients with myocardial ischemia and 52 healthy controls. Results: The ECG + VCG model with three features (S _I ,THI, and SHI) yields better classifying results than ECG-only and VCG-only models with the average accuracy of 0.903, sensitivity of 0.903, specificity of 0.905, F1 score of 0.942, and AUC of 0.904, which shows better performance with fewer features compared with existing works. On the third independent dataset, the testing showed an AUC of 0.814. Conclusion: The SVM algorithm based on the ECG + VCG model could reliably detect myocardial ischemia, providing a potential tool to assist cardiologists in the early diagnosis of CVD in routine screening during primary care services.

Keywords: Lyapunov index; myocardial ischemia; sample entropy (SampEn); support vector machine (SVM); vectorcardiogram (VCG).