DeepTWA-TM: Deep Learning T-Wave Alternans Detection in Ambulatory ECG via Time Analysis.

Abstract

The development of non-invasive markers for assessing the risk of sudden cardiac death has gained significant attention, particularly T-wave alternans (TWA), which can be recorded from surface electrocardiogram (ECG) signals. However, the clinical application of TWA remains insufficiently standardized, complicating its detection in real-world ambulatory environments due to variable conditions that often affect ECG recordings, including dynamic changes, noise, and artifacts. This study presents a Deep Learning (DL) approach designed to detect TWA directly from ECG signals, using transfer learning with robust architectures such as VGG, ResNet, and Inception. Our method simplifies the detection pipeline by eliminating the need for prior signal processing steps such as R-peak identification, T-wave segmentation, or feature engineering. Our models are trained on a custom long-term dataset of real patients, capturing TWA episodes ranging from non-visible micro-alternans to higher amplitude TWA of 20 to 100 $\mu$V, and incorporating a robust methodology that emphasizes patient separation during training and testing to enhance generalizability. The results demonstrate that our model achieves an F1-score of 0.83 during ambulatory analysis, outperforming traditional Machine Learning approaches. By eliminating the need for extensive preprocessing, our approach not only enhances the adaptability of TWA detection but also brings the model closer to practical applicability in clinical settings, leading to more efficient and effective risk stratification for sudden cardiac death.