Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909361460215808 |
|---|---|
| author | Zhu, Xiangqian Shi, Mengnan Yu, Xuexin Liu, Chang Lian, Xiaocong Fei, Jintao Luo, Jiangying Jin, Xin Zhang, Ping Ji, Xiangyang |
| author_facet | Zhu, Xiangqian Shi, Mengnan Yu, Xuexin Liu, Chang Lian, Xiaocong Fei, Jintao Luo, Jiangying Jin, Xin Zhang, Ping Ji, Xiangyang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18094 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation Zhu, Xiangqian Shi, Mengnan Yu, Xuexin Liu, Chang Lian, Xiaocong Fei, Jintao Luo, Jiangying Jin, Xin Zhang, Ping Ji, Xiangyang Quantitative Methods Artificial Intelligence Machine Learning Signal Processing 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. |
| title | Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation |
| topic | Quantitative Methods Artificial Intelligence Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2410.18094 |