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In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection
March 28, 2024, 4:43 a.m. | Sahar Soltanieh, Javad Hashemi, Ali Etemad
cs.LG updates on arXiv.org arxiv.org
Abstract: This paper presents a systematic investigation into the effectiveness of Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by conducting a novel analysis of the data distributions on three popular ECG-based arrhythmia datasets: PTB-XL, Chapman, and Ribeiro. To the best of our knowledge, our study is the first to quantitatively explore and characterize these distributions in the area. We then perform a comprehensive set of experiments using different augmentations and parameters to …
abstract analysis arxiv best of cs.ai cs.lg data datasets detection distribution eess.sp investigation novel paper popular representation representation learning self-supervised learning ssl supervised learning type
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