Key result
The SwAV self-supervised learning method consistently outperformed fully supervised baselines for ECG-based arrhythmia detection across both in-distribution and out-of-distribution datasets.
Why the study?
The effectiveness and data distribution characteristics of self-supervised learning methods for ECG arrhythmia detection across in-distribution and out-of-distribution datasets had not been quantitatively characterized.
Does Self-Supervised Learning (SSL) improve or match supervised methods for arrhythmia detection and generalization across out-of-distribution ECG datasets?
Does Self-Supervised Learning (SSL) improve or match supervised methods for arrhythmia detection and generalization across out-of-distribution ECG datasets?
Self-supervised learning techniques, particularly SwAV, can learn highly effective representations from unlabeled ECG data that generalize well across different datasets for arrhythmia detection.
Supports further SSL exploration for ECG arrhythmia detection; leaves open prospective clinical validation.
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 evaluate the effectiveness of various SSL methods, namely SimCRL, BYOL, and SwAV, for ECG representation learning, where we observe the best performance achieved by SwAV. Furthermore, our analysis shows that SSL methods achieve highly competitive results to those achieved by supervised state-of-the-art methods. To further assess the performance of these methods on both In-Distribution (ID) and Out-of-Distribution (OOD) ECG data, we conduct cross-dataset training and testing experiments. Our comprehensive experiments show almost identical results when comparing ID and OOD schemes, indicating that SSL techniques can learn highly effective representations that generalize well across different OOD datasets. This finding can have major implications for ECG-based arrhythmia detection. Lastly, to further analyze our results, we perform detailed per-disease studies on the performance of the SSL methods on the three datasets.
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Soltanieh et al. (2023) studied Arrhythmia (n=30,358). Self-Supervised Learning (SwAV) vs. Supervised learning was evaluated on Macro F1 score. The SwAV self-supervised learning method consistently outperformed fully supervised baselines for ECG-based arrhythmia detection across both in-distribution and out-of-distribution datasets.
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