Reliable Detection of Myocardial Ischemia Using Machine Learning Based on Temporal-Spatial Characteristics of
Xiaoye Zhao1,2,3, Jucheng Zhang4, Yinglan Gong5,6
1School of Instrument Science and Opto-Electronic Engineering, Hefei University of Technology, Hefei, China.
Insights
Combining electrocardiograms (ECG) and vectorcardiograms (VCG) significantly improves machine learning for detecting myocardial ischemia, aiding early cardiovascular disease diagnosis. This combined approach offers a reliable tool for cardiologists in primary care screening.
Area of Science:
- Cardiology
- Biomedical Engineering
- Machine Learning
Background:
- Myocardial ischemia, an early symptom of cardiovascular disease (CVD), necessitates reliable detection methods.
- Computer-aided analysis of electrocardiograms (ECG) is crucial for early CVD diagnosis.
- Vectorcardiograms (VCG) offer spatiotemporal characteristics that may enhance ECG-based ischemia detection.
Purpose of the Study:
- To investigate the efficacy of combining ECG and VCG data for improving machine learning-based automatic myocardial ischemia detection.
- To compare the performance of ECG-only, VCG-only, and combined ECG+VCG models.
Main Methods:
- Extracted ST-T segments from 12-lead ECGs and VCGs of 377 myocardial ischemia patients and 52 controls.
- Calculated sample entropy (SampEn), spatial heterogeneity index (SHI), and temporal heterogeneity index (THI).
- Developed Support Vector Machine (SVM) models using selected features for ECG-only, VCG-only, and ECG+VCG approaches, validated with 5-fold cross-validation and tested on an independent dataset.
Main Results:
- The combined ECG+VCG model, utilizing SampEn (lead I), THI, and SHI, achieved superior classification performance (accuracy 0.903, AUC 0.904) compared to ECG-only and VCG-only models.
- The combined model demonstrated better performance with fewer features than existing methods.
- The independent dataset validation yielded an AUC of 0.814 for the best-performing model.
Conclusions:
- The SVM algorithm integrating ECG and VCG data reliably detects myocardial ischemia.
- This combined approach serves as a potential tool for cardiologists in early CVD diagnosis during routine primary care screening.
- The findings highlight the value of combining ECG and VCG for enhanced diagnostic capabilities.
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 , THI, and SHI, where S 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 ,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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