Early detection of myocardial ischemia in 12-lead ECG using deterministic learning and ensemble learning
Qinghua Sun1, Chunmiao Liang1, Tianrui Chen1
1Center for Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China.
Insights
This study developed an ensemble learning model to detect myocardial ischemia using non-diagnostic ECGs. The model integrates dynamic ECG features, achieving high accuracy and generalization for improved cardiovascular disease diagnosis.
Area of Science:
- Cardiology
- Biomedical Engineering
- Machine Learning
Background:
- Early detection of myocardial ischemia is crucial for cardiovascular disease management but challenging with standard ECGs.
- Classical ST and T wave analysis on 12-lead ECGs often lacks sufficient accuracy for subtle ischemia.
- Non-diagnostic ECGs present a significant hurdle in identifying myocardial ischemia accurately.
Purpose of the Study:
- To develop generalizable models for detecting myocardial ischemia in patients with non-diagnostic ECGs.
- To integrate dynamic ECG features using deterministic learning and ensemble methods.
- To improve the accuracy of myocardial ischemia detection beyond traditional ECG interpretations.
Main Methods:
- Generated cardiodynamicsgrams (CDG) using deterministic learning from ECG signals.
- Extracted spectral fitting exponent, Lyapunov exponent, and Lempel-Ziv complexity from CDG.
- Applied a bagging-based heterogeneous ensemble algorithm for myocardial ischemia detection on a clinical dataset of 499 non-diagnostic ECGs.
Main Results:
- Achieved an average accuracy of 89.10%, sensitivity of 91.72%, and specificity of 82.69% on the clinical dataset.
- Demonstrated accuracy over 82% across three independent medical centers for non-diagnostic ECGs.
- Validated performance on the public PTB dataset with 91.11% accuracy, 90.49% sensitivity, and 92.88% specificity.
Conclusions:
- The proposed model combining ensemble and deterministic learning shows excellent diagnostic accuracy and generalization.
- This approach can serve as a valuable complement to standard ECG for clinical diagnosis of myocardial ischemia.
- The findings suggest a promising new tool for identifying myocardial ischemia in challenging cases.
Background And Objective:
Early detection of myocardial ischemia is a necessary but difficult problem in cardiovascular diseases. Approaches that exclusively rely on classical ST and T wave changes on the standard 12-lead electrocardiogram (ECG) lack sufficient accuracy in detecting myocardial ischemia. This study aims to construct generalizable models for the detection of myocardial ischemia in patients with subtle ECG waveform changes (namely non-diagnostic ECG) using ensemble learning to integrate ECG dynamic features acquired via deterministic learning.
Methods:
First, cardiodynamicsgram (CDG), a noninvasive spatiotemporal electrocardiographic method, is generated through dynamic modeling of ECG signals using the deterministic learning algorithm. Then, the spectral fitting exponent, Lyapunov exponent, and Lempel-Ziv complexity are extracted from CDG. Subsequently, the bagging-based heterogeneous ensemble algorithm is applied on CDG features to generate diverse base classifiers and aggregate them with weighted voting to obtain an ensemble model for myocardial ischemia detection. Finally, we train and test the proposed heterogeneous ensemble model on a real-world clinical dataset. This dataset consists of 499 non-diagnostic 12-lead ECG records from 499 patients collected from three independent medical centers, including 383 patients with myocardial ischemia and 116 patients without ischemia.
Results:
With 10-times 5-fold cross-validation technology, our proposed method achieves an average accuracy of 89.10%, sensitivity of 91.72%, and specificity of 82.69% using the heterogeneous ensemble algorithm on the real-world clinical dataset. On three independent medical centers, our ensemble model also achieves accuracy performance over 82% for patients with non-diagnostic ECG. Furthermore, our ensemble model trained with real-world clinical data yields promising results of 91.11% accuracy, 90.49% sensitivity, and 92.88% specificity on the external test set of the public PTB dataset.
Conclusion:
The experimental results demonstrate that the proposed model combining ensemble learning and deterministic learning presents excellent diagnostic accuracy and generalization in clinical practice, and could be implemented as a complement to the standard ECG in the clinical diagnosis of myocardial ischemia.
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