sensors Article Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG Hongqiang Li 1,* , Zhixuan An 1 , Shasha Zuo 2, Wei Zhu 2, Zhen Zhang 3, Shanshan Zhang 1,4, Cheng Zhang 1, Wenchao Song 1, Quanhua Mao 1, Yuxin Mu 1, Enbang Li 5 and Juan Daniel Prades García 6 1 Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electrical and Electronic Engineering, Tiangong University, Tianjin 300387, China;
[email protected] (Z.A.);
[email protected] (S.Z.);
[email protected] (C.Z.);
[email protected] (W.S.);
[email protected] (Q.M.);
[email protected] (Y.M.) 2 Textile Fiber Inspection Center, Tianjin Product Quality Inspection Technology Research Institute, Tianjin 300192, China;
[email protected] (S.Z.);
[email protected] (W.Z.) 3 School of Computer Science and Technology, Tiangong University, Tianjin 300387, China;
[email protected] 4 Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Institute of Modern Optics, Nankai University, Tianjin 300071, China 5 Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW 2522, Australia;
[email protected] 6 Institute of Nanoscience and Nanotechnology (IN2UB), Universitat de Barcelona (UB), E-08028 Barcelona, Spain;
[email protected] * Correspondence:
[email protected] Abstract: Heart disease is the leading cause of death for men and women globally. The residual Citation: Li, H.; An, Z.; Zuo, S.; network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our under- Zhu, W.; Zhang, Z.; Zhang, S.; standing of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based Zhang, C.; Song, W.; Mao, Q.; Mu, Y.; on an improved ResNet for a wearable ECG.