文献类型：期刊文献

英文题名：Real Time Arrhythmia Monitoring and Classification Based on Edge Computing and DNN

作者：Liu, Mingxin[1];Shao, Ningning[2];Zheng, Chaoxuan[3];Wang, Ji[1]

机构：[1]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Yanshan Univ, Coll Informat Sci & Engn, Qinhuangdao 006004, Hebei, Peoples R China;[3]Guangzhou Univ, Sch Elect & Commun Engn, Guangzhou 510006, Peoples R China

年份：2021

卷号：2021

外文期刊名：WIRELESS COMMUNICATIONS & MOBILE COMPUTING

收录：SCI-EXPANDED(收录号：WOS:000685139500004)、、EI(收录号：20212310464186)、Scopus(收录号：2-s2.0-85107224526)、WOS

基金：This work was partly supported by the National Natural Science Foundation of China (61871645, J2024023). Innovation Training Program for College Students of Guangzhou University (Provincial) under grant S202011078027.

语种：英文

外文关键词：Neural network models - Convolutional neural networks - Diseases - Deep neural networks

外文摘要：In this paper, we investigate how to incorporate intelligence into the human-centric IoT edges to detect arrhythmia, a heart condition often associated with morbidity and even mortality. We propose a classification algorithm based on the intrapatient convolutional neural network model and the interpatient attention residual network model to automatically identify the type of arrhythmia in the edges. As the imbalance categories in the MIT-BIH arrhythmia database which needs to be used in the algorithm, we slice and overlap the original ECG signal to homogenize the heartbeat sets of different types, and then the preprocessed data was used to train the two proposed network models; the results reached an overall accuracy rate of 99.03% and an F1 value of 0.87, respectively. The proposed algorithm model can be used as a real-time diagnostic tool for the remote E-health system in next generation wireless communication networks.