Gml-PAF: A Generalizable Machine Learning Algorithm for Paroxysmal Atrial Fibrillation Detection based on Short-Term Inter-Beat Intervals

Reliable detection of paroxysmal atrial fibrillation (PAF) poses a significant challenge. We propose a generalizable machine learning (ML) algorithm for PAF detection (Gml-PAF) that uses 21-beat inter-beat intervals (IBI). Gml-PAF employs a model-agnostic framework integrating model selection, feature selection, and hyperparameter tuning. It is trained and evaluated across 16 PhysioNet electrocardiogram (ECG) databases, demonstrating robust cross-database generalization. In independent tests, it achieves F1 scores of 0.747-0.987 and AUC values of 0.933-0.999. The algorithm matches deep learning (DL) performance with longer IBI sequences and surpasses conventional ML methods, confirming its strong utility for wearable screening.