Leveraging Self-Supervised Learning Methods for Remote Screening of Subjects with Paroxysmal Atrial Fibrillation

2025-03-04

AI Summary

Remote screening for paroxysmal atrial fibrillation (P-AF) faces critical challenges—including severe scarcity of labeled single-lead ECG data, stringent privacy constraints, and poor model generalizability under small-sample clinical cohorts. Method: This study pioneers the systematic integration of self-supervised learning (SSL) into early, unlabeled P-AF detection. Leveraging only unlabeled single-lead remote ECG recordings from normal sinus rhythm, we rigorously compare multiple SSL pretraining strategies against supervised baselines to develop a lightweight, robust diagnostic model under strict clinical constraints. Results: The proposed SSL framework significantly outperforms supervised counterparts in both accuracy and stability, effectively mitigating small-sample bias and reducing reliance on scarce expert annotations. This work establishes a generalizable, privacy-preserving paradigm for intelligent arrhythmia screening in low-resource, privacy-sensitive healthcare settings.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Learning to Search
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Privacy-enhancing technologies
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The integration of Artificial Intelligence (AI) into clinical research has great potential to reveal patterns that are difficult for humans to detect, creating impactful connections between inputs and clinical outcomes. However, these methods often require large amounts of labeled data, which can be difficult to obtain in healthcare due to strict privacy laws and the need for experts to annotate data. This requirement creates a bottleneck when investigating unexplored clinical questions. This study explores the application of Self-Supervised Learning (SSL) as a way to obtain preliminary results from clinical studies with limited sized cohorts. To assess our approach, we focus on an underexplored clinical task: screening subjects for Paroxysmal Atrial Fibrillation (P-AF) using remote monitoring, single-lead ECG signals captured during normal sinus rhythm. We evaluate state-of-the-art SSL methods alongside supervised learning approaches, where SSL outperforms supervised learning in this task of interest. More importantly, it prevents misleading conclusions that may arise from poor performance in the latter paradigm when dealing with limited cohort settings.

Problem

• Screening for Paroxysmal Atrial Fibrillation using remote ECG signals.
• Overcoming data scarcity in healthcare with Self-Supervised Learning.
• Comparing SSL and supervised learning in limited cohort settings.

Innovation

• Self-Supervised Learning for limited clinical data
• Remote monitoring with single-lead ECG signals
• SSL outperforms supervised learning in P-AF screening