DeepBoost-AF: A Novel Unsupervised Feature Learning and Gradient Boosting Fusion for Robust Atrial Fibrillation Detection in Raw ECG Signals

• URL: http://arxiv.org/abs/2505.24085v1
• Date: Fri, 30 May 2025 00:08:56 GMT
• Title: DeepBoost-AF: A Novel Unsupervised Feature Learning and Gradient Boosting Fusion for Robust Atrial Fibrillation Detection in Raw ECG Signals
• Authors: Alireza Jafari, Fereshteh Yousefirizi, Vahid Seydi,
• Abstract summary: Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with elevated health risks.<n>This study introduces an innovative hybrid methodology integrating unsupervised deep learning and gradient boosting models to improve AF detection.
• Score: 1.794794261751548
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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