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

TL;DR

This paper presents a hybrid deep learning and gradient boosting approach for automatic atrial fibrillation detection from raw ECG signals, achieving high accuracy and rapid inference suitable for clinical use.

Contribution

It introduces a novel end-to-end framework combining a deep autoencoder with multiple gradient boosting classifiers for AF detection without manual feature extraction.

Findings

F1-score of 95.20% achieved

Sensitivity of 99.99% demonstrated

Inference latency of four seconds

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.