Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China.
Textile Fiber Inspection Center, Tianjin Product Quality Inspection Technology Research Institute, Tianjin 300192, China.
Sensors (Basel). 2021 Sep 9;21(18):6043. doi: 10.3390/s21186043.
Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%.
心脏病是全球男性和女性的主要死因。心电图（ECG）技术的残差网络（ResNet）演进有助于我们理解心脏生理学。我们提出了一种基于改进的 ResNet 的人工智能心电图算法，用于可穿戴心电图。该系统硬件包括带有导电织物电极的可穿戴心电图、无线心电图采集模块、移动终端应用程序和云诊断平台。本研究采用的算法基于改进的 ResNet，用于快速分类不同类型的心律失常。首先，我们可视化 ECG 数据，并使用 Gramian 角场将一维 ECG 信号转换为二维图像。然后，我们改进 ResNet-50 网络模型，在网络中添加多级快捷分支，并优化残差块。将 ReLu 激活函数替换为缩放指数线性单元（SELUs）激活函数，以提高模型的表达能力。最后，将图像输入到改进的 ResNet 网络进行分类。该分类算法对七种心律失常信号（心房颤动、房性早搏、室性早搏、正常搏动、室性心动过速、房性心动过速和窦性心动过缓）的平均识别率为 98.3%。
