Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG
Hongqiang Li,
Zhixuan An,
Shasha Zuo,
Wei Zhu,
Zhen Zhang,
Shanshan Zhang,
Cheng Zhang,
Wenchao Song,
Quanhua Mao,
Yuxin Mu,
Enbang Li,
Juan Daniel Prades García
Affiliations
Hongqiang Li
Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China
Zhixuan An
Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China
Shasha Zuo
Textile Fiber Inspection Center, Tianjin Product Quality Inspection Technology Research Institute, Tianjin 300192, China
Wei Zhu
Textile Fiber Inspection Center, Tianjin Product Quality Inspection Technology Research Institute, Tianjin 300192, China
Zhen Zhang
School of Computer Science and Technology, Tiangong University, Tianjin 300387, China
Shanshan Zhang
Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China
Cheng Zhang
Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China
Wenchao Song
Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China
Quanhua Mao
Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China
Yuxin Mu
Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China
Enbang Li
Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2522, Australia
Juan Daniel Prades García
Institute of Nanoscience and Nanotechnology (IN2UB), Universitat de Barcelona (UB), E-08028 Barcelona, Spain
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%.
