Key result
Manifold learning matches established TWA detection methods with a ~79% F1-score while enhancing explainability.
Why the study?
Machine learning methods for TWA detection have been limited by their black-box nature, which reduces interpretability.
Manifold learning techniques can maintain T-wave alternans detection performance while enhancing the explainability of machine learning models.
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MnL may enhance ML interpretability for TWA detection; leaves open clinical utility for sudden death risk stratification.
Background T-wave alternans (TWA) refers to variations in the ventricular repolarization pattern observed on the ECG, which has been associated with cardiac instability and an increased risk of sudden cardiac death. Recently, machine learning (ML) methods have been developed for TWA detection, but their black-box nature limits interpretability. Objectives To address this gap, we propose manifold learning (MnL) to enhance the explainability of these learning models while maintaining TWA detection effectiveness. Methods We fine-tuned nonlinear dimension reduction techniques such as Uniform Manifold Approximation and Projection (UMAP), Isometric Mapping (Isomap), and autoencoders (AE) in combination with ML methods, namely K-nearest neighbors (KNN), random forest (RF), and neural networks (NN). Performance was evaluated using mean and standard deviation across patient-wise permutations. Results In the design stage, the AE-based NN effectively retained essential discriminative information (F1-score 92.1 ± 2.4 %). MnL-generated spaces consistently revealed that misclassifications primarily lie close to the decision boundary and are predominantly associated with lower TWA voltages, which are more dispersed within the space. For ambulatory TWA detection, Isomap combined with RF and the AE-based NN achieved performance comparable to using the complete set of features derived from established TWA analysis methods (F1-score 78.5 ± 6.4 % and 77.9 ± 5.4 %, respectively), including spectral, time-domain, and correlation-based descriptors. The latent space visualization shows that predictions that ultimately become detections are located farther away from the decision boundary. Conclusion MnL-generated spaces provide valuable insights into how classification models differentiate between TWA and non-TWA instances, as well as the patterns in TWA event amplitudes. This approach helps bridge the gap between performance and transparency, supporting more clinically reliable TWA detection.
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Pascual-Sánchez et al. (2026) studied T-wave alternans. Manifold learning (MnL) combined with machine learning methods vs. Established TWA analysis methods was evaluated on TWA detection effectiveness (F1-score). Manifold learning techniques like Isomap with random forest achieved an F1-score of 78.5% for ambulatory T-wave alternans detection, comparable to established methods while enhancing explainability.
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