Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with elevated health risks, where timely detection is pivotal for mitigating stroke-related morbidity. This study introduces an innovative hybrid methodology integrating unsupervised deep learning and gradient boosting models to improve AF detection. A 19-layer deep convolutional autoencoder (DCAE) is coupled with three boosting classifiers-AdaBoost, XGBoost, and LightGBM (LGBM)-to harness their complementary advantages while addressing individual limitations. The proposed framework uniquely combines DCAE with gradient boosting, enabling end-to-end AF identification devoid of manual feature extraction. The DCAE-LGBM model attains an F1-score of 95.20%, sensitivity of 99.99%, and inference latency of four seconds, outperforming existing methods and aligning with clinical deployment requirements. The DCAE integration significantly enhances boosting models, positioning this hybrid system as a reliable tool for automated AF detection in clinical settings.
@article{arxiv.2505.24085,
title = {DeepBoost-AF: A Novel Unsupervised Feature Learning and Gradient Boosting Fusion for Robust Atrial Fibrillation Detection in Raw ECG Signals},
author = {Alireza Jafari and Fereshteh Yousefirizi and Vahid Seydi},
journal= {arXiv preprint arXiv:2505.24085},
year = {2025}
}
Comments
12-page,4 figures,3 tables, Achieves 95.20% F1-score (99.99% sensitivity) on 8,528 PhysioNet 2017 recordings, Mean inference time: 4 seconds, Python implementation will be open-sourced upon publication