Lim Jaechan, Han Dong, Chon Ki H
Department of Biomedical Engineering, University of Connecticut, Storrs, 06269, CT, United States.
Department of Biomedical Engineering, University of Connecticut, Storrs, 06269, CT, United States.
Comput Biol Med. 2025 Jun;192(Pt B):110282. doi: 10.1016/j.compbiomed.2025.110282. Epub 2025 May 15.
We propose a deep learning approach for beat-wise atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. AF, a major cardiac arrhythmia affecting millions globally, requires early detection for optimal treatment outcomes. Current rhythm-based methods lack beat-level precision and timeliness. Our QRS-centric approach improves traditional classification by providing precise beat-wise analysis, addressing the critical gap between rhythm-level detection and immediate arrhythmia identification. Our network architecture integrates convolutional layers for feature extraction with bidirectional LSTM layers that target the QRS complex region through adaptive beat segmentation. This architecture enables detailed temporal dependency analysis while preserving morphological feature sensitivity. Extensive evaluation on the MIT-BIH Arrhythmia Database demonstrates exceptional performance with 98.606% precision and 99.648% sensitivity for AF detection. The model's robust generalizability is confirmed through independent validation on the MIT-BIH Atrial Fibrillation Database, achieving a 98.653% F-score. Further validation using contemporary datasets (MIMIC-III and Simband) confirms the model's effectiveness in identifying multiple beat types including normal sinus rhythm, AF, premature ventricular contraction, and premature atrial contraction. The QRS-centric approach enables timely arrhythmia detection at the individual beat level, demonstrating a significant advancement in automated ECG analysis. The high accuracy and beat-level precision of this approach suggest potential for integration with wearable devices and real-time monitoring systems, enabling earlier clinical intervention and improved arrhythmia diagnosis in practice.
我们提出了一种用于在心电图（ECG）信号中逐搏检测心房颤动（AF）的深度学习方法。AF是一种影响全球数百万人的主要心律失常，需要早期检测以获得最佳治疗效果。当前基于节律的方法缺乏逐搏精度和及时性。我们以QRS为中心的方法通过提供精确的逐搏分析改进了传统分类，解决了节律水平检测与即时心律失常识别之间的关键差距。我们的网络架构将用于特征提取的卷积层与双向LSTM层集成在一起，后者通过自适应搏动分割针对QRS复合波区域。这种架构能够在保持形态特征敏感性的同时进行详细的时间依赖性分析。在麻省理工学院-贝斯以色列女执事医疗中心心律失常数据库上进行的广泛评估表明，AF检测的性能卓越，精度为98.606%，灵敏度为99.648%。通过在麻省理工学院-贝斯以色列女执事医疗中心心房颤动数据库上的独立验证，证实了该模型强大的泛化能力，F值达到98.653%。使用当代数据集（MIMIC-III和Simband）进行的进一步验证证实了该模型在识别多种搏动类型方面的有效性，包括正常窦性心律、AF、室性早搏和房性早搏。以QRS为中心的方法能够在个体搏动水平上及时检测心律失常，表明在自动ECG分析方面取得了重大进展。这种方法的高精度和逐搏精度表明，它有可能与可穿戴设备和实时监测系统集成，从而在实际应用中实现更早的临床干预和改善心律失常诊断。
