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http://hdl.handle.net/2445/183966
Title: | Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG |
Author: | Li, Hongqiang An, Zhixuan Zuo, Shasha Zhu, Wei Zhang, Zhen Zhang, Shanshan Zhang, Cheng Song, Wenchao Mao, Quanhua Mu, Yuxin Li, Enbang Prades García, Juan Daniel |
Keywords: | Computació en núvol Intel·ligència artificial Electrocardiografia Cloud computing Artificial intelligence Electrocardiography |
Issue Date: | 1-Sep-2021 |
Publisher: | MDPI |
Abstract: | 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%. |
Note: | Reproducció del document publicat a: https://doi.org/10.3390/s21186043 |
It is part of: | Sensors, 2021, vol. 21, num. 18, p. 6043-6061 |
URI: | http://hdl.handle.net/2445/183966 |
Related resource: | https://doi.org/10.3390/s21186043 |
ISSN: | 1424-8220 |
Appears in Collections: | Articles publicats en revistes (Enginyeria Electrònica i Biomèdica) |
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