1. Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation
- Author
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Zhu, Xiangqian, Shi, Mengnan, Yu, Xuexin, Liu, Chang, Lian, Xiaocong, Fei, Jintao, Luo, Jiangying, Jin, Xin, Zhang, Ping, and Ji, Xiangyang
- Subjects
Quantitative Biology - Quantitative Methods ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Signal Processing - Abstract
Atrial fibrillation is a commonly encountered clinical arrhythmia associated with stroke and increased mortality. Since professional medical knowledge is required for annotation, exploiting a large corpus of ECGs to develop accurate supervised learning-based atrial fibrillation algorithms remains challenging. Self-supervised learning (SSL) is a promising recipe for generalized ECG representation learning, eliminating the dependence on expensive labeling. However, without well-designed incorporations of knowledge related to atrial fibrillation, existing SSL approaches typically suffer from unsatisfactory capture of robust ECG representations. In this paper, we propose an inter-intra period-aware ECG representation learning approach. Considering ECGs of atrial fibrillation patients exhibit the irregularity in RR intervals and the absence of P-waves, we develop specific pre-training tasks for interperiod and intraperiod representations, aiming to learn the single-period stable morphology representation while retaining crucial interperiod features. After further fine-tuning, our approach demonstrates remarkable AUC performances on the BTCH dataset, \textit{i.e.}, 0.953/0.996 for paroxysmal/persistent atrial fibrillation detection. On commonly used benchmarks of CinC2017 and CPSC2021, the generalization capability and effectiveness of our methodology are substantiated with competitive results., Comment: Preprint submitted to Biomedical Signal Processing and Control
- Published
- 2024