Chen Chun-Yen, Lin Yan-Ting, Lee Shie-Jue, Tsai Wei-Chung, Huang Tien-Chi, Liu Yi-Hsueh, Cheng Mu-Chun, Dai Chia-Yen
Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan.
Department of Electrical Engineering and Intelligent Electronic Commerce Research Center, National Sun Yat-Sen University, Kaohsiung, Taiwan.
Methods. 2022 Jun;202:127-135. doi: 10.1016/j.ymeth.2021.04.021. Epub 2021 Apr 27.
The standard 12-lead electrocardiogram (ECG) records the heart's electrical activity from electrodes on the skin, and is widely used in screening and diagnosis of the cardiac conditions due to its low price and non-invasive characteristics. Manual examination of ECGs requires professional medical skills, and is strenuous and time consuming. Recently, deep learning methodologies have been successfully applied in the analysis of medical images. In this paper, we present an automated system for the identification of normal and abnormal ECG signals. A multi-channel multi-scale deep neural network (DNN) model is proposed, which is an end-to-end structure to classify the ECG signals without any feature extraction. Convolutional layers are used to extract primary features, and long short-term memory (LSTM) and attention are incorporated to improve the performance of the DNN model. The system was developed with a 12-lead ECG dataset provided by the Kaohsiung Medical University Hospital (KMUH). Experimental results show that the proposed system can yield high recognition rates in classifying normal and abnormal ECG signals.
标准12导联心电图（ECG）通过皮肤上的电极记录心脏的电活动，因其价格低廉和无创特性而广泛用于心脏疾病的筛查和诊断。手动检查心电图需要专业的医疗技能，既费力又耗时。近年来，深度学习方法已成功应用于医学图像分析。在本文中，我们提出了一种用于识别正常和异常心电图信号的自动化系统。提出了一种多通道多尺度深度神经网络（DNN）模型，它是一种端到端结构，无需任何特征提取即可对心电图信号进行分类。卷积层用于提取主要特征，并结合长短期记忆（LSTM）和注意力机制来提高DNN模型的性能。该系统是利用高雄医学大学附属医院（KMUH）提供的12导联心电图数据集开发的。实验结果表明，所提出的系统在对正常和异常心电图信号进行分类时能够获得较高的识别率。
