轻量模型精准识别心律失常，适配可穿戴设备实时监测
A Lightweight CNN-Attention-BiLSTM Architecture for Multi-Class Arrhythmia Classification on Standard and Wearable ECGs
- 融合1D CNN、注意力与BiLSTM，兼顾特征提取与序列建模
- 在CPSC 2018数据集上准确率与F1值优于基线模型
- 仅0.945万参数，适合部署于资源受限的可穿戴设备
早期精准检测心律失常对及时诊断和干预至关重要。本文提出一种轻量级深度学习模型，结合一维卷积神经网络（1D CNN）、注意力机制与双向长短期记忆网络（BiLSTM），用于从12导联和单导联心电图中分类心律失常。在CPSC 2018数据集上，模型通过类别加权损失缓解类别不平衡问题，表现优于基线模型，在准确率与F1分数上均取得提升。模型参数仅0.945百万，适合在可穿戴健康监测系统中实现实时部署。
原文摘要 · Abstract (English)
Early and accurate detection of cardiac arrhythmias is vital for timely diagnosis and intervention. We propose a lightweight deep learning model combining 1D Convolutional Neural Networks (CNN), attention mechanisms, and Bidirectional Long Short-Term Memory (BiLSTM) for classifying arrhythmias from both 12-lead and single-lead ECGs. Evaluated on the CPSC 2018 dataset, the model addresses class imbalance using a class-weighted loss and demonstrates superior accuracy and F1- scores over baseline models. With only 0.945 million parameters, our model is well-suited for real-time deployment in wearable health monitoring systems.
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