Early detection of myocardial ischemia in 12‐lead ECG using deterministic learning and ensemble learning

• Published in: Computer methods and programs in biomedicine Vol. 226; p. 107124
• Main Authors: Sun, Qinghua, Liang, Chunmiao, Chen, Tianrui, Ji, Bing, Liu, Rugang, Wang, Lei, Tang, Min, Chen, Yuguo, Wang, Cong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2022
• Subjects: Cardiodynamicsgram; Deterministic learning; Electrocardiogram; Ensemble learning; Cardiodynamicsgram; Ensemble learning; Electrocardiogram; Deterministic learning
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2022.107124

•In this study, the heterogeneous ensemble algorithm is used to fuse different ECG dynamic features to construct a generalizable model for detecting myocardial ischemia.•This study further collects more abundant cases with non-diagnostic ECG to construct a real-world clinical data set for the development and validation of effective myocardial ischemia detection methods with clinical generalization ability.•The heterogeneous ensemble model trained on the clinical data set also achieves 91.11% accuracy on the public PTB dataset that is used as an independent test set, which is better than the results of classical ML classifiers (the linear kernel and RBF kernel SVM) and homogeneous ensemble algorithms (boosting tree and random forest).•To our knowledge, this is the first study that validated and tested the performance of dynamic feature-based models on independent clinical data sets to predict myocardial ischemia with non-diagnostic ECG. 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.