Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation

TL;DR

This paper introduces a self-supervised ECG representation learning method that captures inter- and intra-period features, improving atrial fibrillation detection accuracy without relying on extensive labeled data.

Contribution

It proposes a novel inter-intra period-aware pre-training approach tailored for atrial fibrillation ECGs, enhancing the robustness of learned representations.

Findings

Achieved AUC of 0.953/0.996 on BTCH dataset for paroxysmal/persistent AF.

Demonstrated strong generalization on CinC2017 and CPSC2021 benchmarks.

Outperformed existing SSL methods in AF detection accuracy.

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.