文献类型：期刊文献

英文题名：A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection

作者：Shao, Minggang[1,2];Zhou, Zhuhuang[1];Bin, Guangyu[1];Bai, Yanping[1];Wu, Shuicai[1]

第一作者：Shao, Minggang

通讯作者：Wu, SC[1]

机构：[1]Beijing Univ Technol, Coll Life Sci & Bioengn, Beijing 100124, Peoples R China;[2]Beijing Union Univ, Smart City Coll, Beijing 100101, Peoples R China

第一机构：Beijing Univ Technol, Coll Life Sci & Bioengn, Beijing 100124, Peoples R China

通讯机构：[1]corresponding author), Beijing Univ Technol, Coll Life Sci & Bioengn, Beijing 100124, Peoples R China.

年份：2020

卷号：20

期号：3

外文期刊名：SENSORS

收录：;EI(收录号：20200708178932);Scopus(收录号：2-s2.0-85078265110);WOS:【SCI-EXPANDED(收录号:WOS:000517786200030)】；

基金：This study was supported partially by the National Natural Science Foundation of China, grant numbers 71661167001, 71781260096 and 61871005, by the Beijing Natural Science Foundation, grant number 4184081, by the General Project of Science and Technology Program of Beijing Municipal Education Commission, grant number KM201911417011, by the International Research Cooperation Seed Fund of Beijing University of Technology, grant number 2018A15, and by the Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation.

语种：英文

外文关键词：electrocardiogram (ECG) monitoring; wearable ECG patch; atrial fibrillation detection; Android smartphone; cloud computing

摘要：In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor's diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F-1 score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.