Zhou Feiyan, Fang Duanshu
Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China.
Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China.
Sci Rep. 2025 May 12;15(1):16418. doi: 10.1038/s41598-025-94127-6.
Detecting and classifying arrhythmias is essential in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often encounter difficulties in effectively integrating both the morphological and temporal features of Electrocardiograms (ECGs). To address this challenge, we propose a Convolutional Neural Network (CNN) that incorporates mixed scales and hierarchical features combined with the Lead Encoder Attention (LEA) mechanism for multi-lead ECG classification. We validated the performance of our proposed method using the intrapatient approach of the MIT-BIH Arrhythmia (MIT-BIH-AR) Database and the interpatient approach of the Chinese Cardiovascular Disease Database (CCDD). Our model achieves an Accuracy (Acc) of 99.5% for the classification of normal and abnormal heartbeats in the MIT-BIH-AR database. Our method achieves a TPR95 (NPV under the condition of True Positive Rate being equal to 95 percent) of 78.5% and an Acc of 88.5% when classifying normal and abnormal ECG records from over 150,000 ECG records in the CCDD. The cross-dataset experimental results also confirm the model's strong generalization capability.
检测和分类心律失常对于诊断心血管疾病至关重要。然而，当前基于深度学习的分类方法在有效整合心电图（ECG）的形态学和时间特征方面常常遇到困难。为应对这一挑战，我们提出了一种卷积神经网络（CNN），该网络结合了混合尺度和层次特征，并与导联编码器注意力（LEA）机制相结合，用于多导联心电图分类。我们使用麻省理工学院-比哈尔心律失常（MIT-BIH-AR）数据库的患者内方法和中国心血管疾病数据库（CCDD）的患者间方法验证了我们提出的方法的性能。我们的模型在MIT-BIH-AR数据库中对正常和异常心跳分类的准确率（Acc）达到了99.5%。在对CCDD中超过150,000份心电图记录进行正常和异常心电图记录分类时，我们的方法实现了78.5%的TPR95（真阳性率等于95%时的阴性预测值）和88.5%的Acc。跨数据集实验结果也证实了该模型强大的泛化能力。
