用深度自编码+梯度提升，自动识别心电图房颤，准确率超95%。
DeepBoost-AF: A Novel Unsupervised Feature Learning and Gradient Boosting Fusion for Robust Atrial Fibrillation Detection in Raw ECG Signals
- 先用19层自编码器无监督提取心电信号特征，再用LightGBM分类
- 模型F1值达95.20%，灵敏度接近100%，推理仅需4秒
- 适合临床部署，无需人工设计特征，检测结果可靠
房颤是一种常见且高危的心律失常，及时检测对降低中风风险至关重要。本文提出一种融合无监督深度学习与梯度提升的新型混合方法，用于从原始心电图信号中检测房颤。采用19层深度卷积自编码器（DCAE）与AdaBoost、XGBoost、LightGBM三种提升分类器结合，发挥各自优势并弥补不足。该框架创新性地将DCAE与梯度提升模型联合，实现端到端的房颤识别，无需人工特征提取。其中DCAE-LGBM模型取得95.20%的F1分数、99.99%的灵敏度，推理延迟仅为4秒，优于现有方法，满足临床部署要求。DCAE的引入显著提升了分类性能，使该混合系统成为临床环境中可靠的自动化房颤检测工具。
原文摘要 · Abstract (English)
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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