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In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection

Sahar Soltanieh, Javad Hashemi, Ali Etemad

TL;DR

This paper presents a systematic investigation into the effectiveness of Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection, and shows that SSL techniques can learn highly effective representations that generalize well across different OOD datasets.

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 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.

In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection

TL;DR

This paper presents a systematic investigation into the effectiveness of Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection, and shows that SSL techniques can learn highly effective representations that generalize well across different OOD datasets.

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 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.
Paper Structure (16 sections, 6 equations, 10 figures, 10 tables)

This paper contains 16 sections, 6 equations, 10 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Background and Literature Review
  3. Methodology and Experiment Setup
  4. Self-supervised Learning Techniques
  5. SimCLR
  6. BYOL
  7. SwAV
  8. Datasets
  9. Implementation Details and Evaluation Protocol
  10. Augmentation Details
  11. Data Distribution
  12. Results and Discussions
  13. Data Distribution Analysis
  14. SSL Method Performance
  15. Per-Disease Analysis
  16. ...and 1 more sections

Figures (10)

  • Figure 1: An overview of SimCLR.
  • Figure 2: An overview of BYOL.
  • Figure 3: An overview of SwAV.
  • Figure 4: Data distribution analysis framework.
  • Figure 5: Visual representation of the distribution of the datasets. Part (a) illustrates the distributions of PTB-XL train and test subsets (ID) and part (b) shows the OOD instances (Chapman and Ribeiro) with respect to PTB-XL dataset.
  • ...and 5 more figures