用注意力增强的VAE-BiLSTM模型，提升12导联心电图异常检测精度。
An Attention-Augmented VAE-BiLSTM Framework for Anomaly Detection in 12-Lead ECG Signals
- 融合注意力机制的VAE-BiLSTM架构，捕捉心电波形时空特征。
- 在CPSC数据集上达AUPRC 0.81、召回率0.85，优于其他模型。
- 支持临床部署，可交互可视化异常位置，适合医疗辅助诊断。
12导联心电图异常检测对识别心血管疾病相关偏差至关重要。本文比较了三种基于自编码器的架构：卷积自编码器（CAE）、变分自编码器与双向LSTM结合（VAE-BiLSTM），以及加入多头注意力机制的VAE-BiLSTM（VAE-BiLSTM-MHA），用于无监督心电图异常检测。据我们所知，该研究首次将VAE-BiLSTM-MHA应用于心电图异常检测。所有模型均在正常心电图样本上训练，以重建非异常心脏形态并检测疾病征兆。在公开的中国生理信号挑战赛（CPSC）数据集上，采用统一预处理与评估流程，注意力增强型VAE表现最佳，测试集上达到AUPRC 0.81和召回率0.85，优于其他架构。为支持临床分诊，该模型进一步集成至交互式仪表盘，实现异常定位可视化。同时，与文献中基线模型进行了性能对比。
原文摘要 · Abstract (English)
Anomaly detection in 12-lead electrocardiograms (ECGs) is critical for identifying deviations associated with cardiovascular disease. This work presents a comparative analysis of three autoencoder-based architectures: convolutional autoencoder (CAE), variational autoencoder with bidirectional long short-term memory (VAE-BiLSTM), and VAE-BiLSTM with multi-head attention (VAE-BiLSTM-MHA), for unsupervised anomaly detection in ECGs. To the best of our knowledge, this study reports the first application of a VAE-BiLSTM-MHA architecture to ECG anomaly detection. All models are trained on normal ECG samples to reconstruct non-anomalous cardiac morphology and detect deviations indicative of disease. Using a unified preprocessing and evaluation pipeline on the public China Physiological Signal Challenge (CPSC) dataset, the attention-augmented VAE achieves the best performance, with an AUPRC of 0.81 and a recall of 0.85 on the held-out test set, outperforming the other architectures. To support clinical triage, this model is further integrated into an interactive dashboard that visualizes anomaly localization. In addition, a performance comparison with baseline models from the literature is provided.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。