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Jiang M, Bian F, Zhang J, Huang T, Xia L, Chu Y, Wang Z, Jiang J. Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features. Physiol Meas 2024; 45:055017. [PMID: 38697203 DOI: 10.1088/1361-6579/ad46e1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 05/02/2024] [Indexed: 05/04/2024]
Abstract
Objective.Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).Approach.A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed. This method consists of three main parts: (1) The onset of MI is often accompanied by changes in the shape of the T-wave in the ECG, thus the area of the T-wave displayed on different heartbeats will be quite different. The adaptive sliding window method is used to detect the start and end of the T-wave, and calculate the C_TWA on the same ECG record. Additionally, the coefficient of variation of C_TWA is defined as the C_TWA feature of the ECG. (2) The multi lead fusion convolutional neural network was implemented to extract the deep features of the ECG. (3) The C_TWA feature and deep features of the ECG were fused by soft attention, and then inputted into the multi-layer perceptron to obtain the detection result.Main results.According to the inter-patient paradigm, the proposed method reached a 97.67% accuracy, 96.59% precision, and 98.96% recall on the PTB dataset, as well as reached 93.15% accuracy, 93.20% precision, and 95.14% recall on the clinical dataset.Significance.This method accurately extracts the feature of the C_TWA, and combines the deep features of the signal, thereby improving the detection accuracy and achieving favorable results on clinical datasets.
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Affiliation(s)
- Mingfeng Jiang
- School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, People's Republic of China
| | - Feibiao Bian
- School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, People's Republic of China
| | - Jucheng Zhang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, People's Republic of China
| | - Tianhai Huang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Ling Xia
- Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou, People's Republic of China
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, People's Republic of China
| | - Yonghua Chu
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhikang Wang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of China
| | - Jun Jiang
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, People's Republic of 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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