Interpretable manifold learning for T-wave alternans assessment with electrocardiographic imaging

Abstract

T-wave alternans (TWA) is a biomarker for sudden cardiac death prediction, characterized by subtle variations in the amplitude or morphology of consecutive T-waves in electrocardiographic studies. Electrocardiographic imaging (ECGI) offers increased spatial resolution, enabling TWA distribution analysis across the epicardium. However, existing TWA estimation methods disregard ECGI spatial information by analyzing each signal independently. To address this gap, we present a novel, subject-specific, interpretable manifold learning-based TWA estimation method tailored to ECGI. First, Uniform Manifold Approximation and Projection (UMAP) reduces input data dimensions. Second, the Louvain algorithm detects communities and identifies the TWA-dominant community. Finally, the location of this community and the rest of the communities is compared, and a Bootstrap-based TWA classifier is applied. A customized Shapley additive explanations method was developed to identify the signal segments most affecting the algorithm decisions to enhance explainability. Reducing the input data to 18 dimensions improved the separation of the TWA-dominant community, with an average normalized distance of 0.28. The Bootstrap analysis showed that the TWA-dominant community had a distance metric up to 0.2 above the confidence interval upper limit. The TWA-dominant community input signals showed different TWA patterns, namely, hump-shaped and amplitude-shifted TWA, and the interpretability algorithm revealed that UMAP focuses on them when projecting points into the latent space. Our method achieved maximum accuracy in subjects with known outcomes and made consistent patient decisions based on input signals. This study introduces the first ECGI-specific TWA detection method. Its subject-specific nature enables the extraction of individual-specific characteristics, offering personalized diagnostic insights