Core Concepts
Self-supervised learning methods show promise in ECG arrhythmia detection, with SwAV outperforming SimCLR and BYOL.
Abstract
The paper investigates the effectiveness of Self-Supervised Learning (SSL) methods for ECG arrhythmia detection. It analyzes data distributions on popular ECG-based arrhythmia datasets and evaluates SSL methods like SimCRL, BYOL, and SwAV. The study shows that SSL methods achieve competitive results compared to supervised methods and can generalize well across different datasets. SwAV consistently performs the best, indicating its potential for ECG-based arrhythmia detection. The study also delves into cross-dataset training and testing experiments to assess model generalization.
Data Distributions Analysis:
- UMAP analysis reveals PTB-XL train and test sets are ID, while Chapman and Ribeiro datasets are OOD.
- Overlap between PTB-XL train and test sets is 83.52%, while overlap with Chapman and Ribeiro datasets is 63.65% and 46.36% respectively.
- Chapman train and test sets overlap by 82.52%, while overlap with PTB-XL and Ribeiro datasets is 67.73% and 60.29% respectively.
SSL Method Performance:
- SwAV consistently outperforms SimCLR and BYOL in arrhythmia classification across datasets.
- SwAV achieves higher f1 scores in both ID and OOD settings compared to SimCLR and BYOL.
- Results confirm the effectiveness of SSL methods for ECG arrhythmia detection, with SwAV showing the best performance.
Stats
To the best of our knowledge, our study is the first to quantitatively explore and characterize these distributions in the area.
Our comprehensive experiments show almost identical results when comparing ID and OOD schemes.
SwAV consistently achieves the best overall results for ECG arrhythmia detection across all explored datasets in both ID and OOD settings.
Quotes
"Our findings provide valuable insights into the capabilities and limitations of SSL in arrhythmia ECG analysis."