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Zhao X, Gong Y, Xu L, Xia L, Zhang J, Zheng D, Yao Z, Zhang X, Wei H, Jiang J, Liu H, Mao J. Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:13061-13085. [PMID: 37501478 DOI: 10.3934/mbe.2023582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
PURPOSE Coronary microvascular dysfunction (CMD) is emerging as an important cause of myocardial ischemia, but there is a lack of a non-invasive method for reliable early detection of CMD. AIM To develop an electrocardiogram (ECG)-based machine learning algorithm for CMD detection that will lay the groundwork for patient-specific non-invasive early detection of CMD. METHODS Vectorcardiography (VCG) was calculated from each 10-second ECG of CMD patients and healthy controls. Sample entropy (SampEn), approximate entropy (ApEn), and complexity index (CI) derived from multiscale entropy were extracted from ST-T segments of each lead in ECGs and VCGs. The most effective entropy subset was determined using the sequential backward selection algorithm under the intra-patient and inter-patient schemes, separately. Then, the corresponding optimal model was selected from eight machine learning models for each entropy feature based on five-fold cross-validations. Finally, the classification performance of SampEn-based, ApEn-based, and CI-based models was comprehensively evaluated and tested on a testing dataset to investigate the best one under each scheme. RESULTS ApEn-based SVM model was validated as the optimal one under the intra-patient scheme, with all testing evaluation metrics over 0.8. Similarly, ApEn-based SVM model was selected as the best one under the intra-patient scheme, with major evaluation metrics over 0.8. CONCLUSIONS Entropies derived from ECGs and VCGs can effectively detect CMD under both intra-patient and inter-patient schemes. Our proposed models may provide the possibility of an ECG-based tool for non-invasive detection of CMD.
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Affiliation(s)
- Xiaoye Zhao
- School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, Anhui, China
- School of Electrical and Information Engineering, North Minzu University, Yinchuan 750001, Ningxia, China
- Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, Yinchuan 750001, Ningxia, China
| | - Yinlan Gong
- Institute of Wenzhou, Zhejiang University, Wenzhou 325000, Zhejiang, China
| | - Lihua Xu
- Hangzhou Linghua Biotech Ltd, Hangzhou 310009, Zhejiang, China
| | - Ling Xia
- Key Laboratory for Biomedical Engineering of Ministry of Education, Hangzhou 310009, Zhejiang, China
- Institute of Biomedical Engineering, Zhejiang University, Hangzhou 310009, Zhejiang, China
| | - Jucheng Zhang
- Department of Clinical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, United Kingdom
| | - Zongbi Yao
- Department of Cardiology, Ningxia Hui Autonomous Region People's Hospital, Yinchuan 750021, Ningxia, China
| | - Xinjie Zhang
- Department of Cardiology, Ningxia Hui Autonomous Region People's Hospital, Yinchuan 750021, Ningxia, China
| | - Haicheng Wei
- School of Electrical and Information Engineering, North Minzu University, Yinchuan 750001, Ningxia, China
| | - Jun Jiang
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang, China
| | - Haipeng Liu
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, United Kingdom
| | - Jiandong Mao
- School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, Anhui, China
- School of Electrical and Information Engineering, North Minzu University, Yinchuan 750001, Ningxia, China
- Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, Yinchuan 750001, Ningxia, China
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Zhao X, Zhang J, Gong Y, Xu L, Liu H, Wei S, Wu Y, Cha G, Wei H, Mao J, Xia L. Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of Electrocardiogram and Vectorcardiogram. Front Physiol 2022; 13:854191. [PMID: 35707012 PMCID: PMC9192098 DOI: 10.3389/fphys.2022.854191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/12/2022] [Indexed: 11/15/2022] Open
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 (SI, THI, and SHI, where SI 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 (SI,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.
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Affiliation(s)
- Xiaoye Zhao
- School of Instrument Science and Opto-Electronic Engineering, Hefei University of Technology, Hefei, China.,School of Electrical and Information Engineering, North Minzu University, Yinchuan, China.,Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, Yinchuan, China
| | - Jucheng Zhang
- Department of Clinical Engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yinglan Gong
- Hangzhou Maixin Technology Co., Ltd., Hangzhou, China.,Institute of Wenzhou, Zhejiang University, Wenzhou, China
| | - Lihua Xu
- Hangzhou Linghua Biotech Ltd., Hangzhou, China
| | - Haipeng Liu
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
| | - Shujun Wei
- Department of Cardiology, Ningxia Hui Autonomous Region People's Hospital, Yinchuan, China
| | - Yuan Wu
- Department of Cardiology, Ningxia Hui Autonomous Region People's Hospital, Yinchuan, China
| | - Ganhua Cha
- School of Electrical and Information Engineering, North Minzu University, Yinchuan, China
| | - Haicheng Wei
- School of Electrical and Information Engineering, North Minzu University, Yinchuan, China
| | - Jiandong Mao
- School of Instrument Science and Opto-Electronic Engineering, Hefei University of Technology, Hefei, China.,School of Electrical and Information Engineering, North Minzu University, Yinchuan, China.,Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, Yinchuan, China
| | - Ling Xia
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou, China
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