文献类型：期刊文献

英文题名：Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features

作者：Shao, Minggang[1,2];Bin, Guangyu[1];Wu, Shuicai[1];Bin, Guanghong[1];Huang, Jiao[1];Zhou, Zhuhuang[1]

第一作者：Shao, Minggang

通讯作者：Bin, GY[1]

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

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

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

年份：2018

卷号：39

期号：9

外文期刊名：PHYSIOLOGICAL MEASUREMENT

收录：;EI(收录号：20215211390317);Scopus(收录号：2-s2.0-85054734882);WOS:【SCI-EXPANDED(收录号:WOS:000445951900002)】；

基金：The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (No. 71661167001). ZZ was supported by Beijing Natural Science Foundation (Grant No. 4184081), China Postdoctoral Science Foundation (Grant No. 2017M620566), Postdoctoral Research Fund of Chaoyang District, Beijing (Grant No. 2017ZZ-01-03), and Basic Research Fund of Beijing University of Technology.

语种：英文

外文关键词：atrial fibrillation; decision tree ensemble; multi-level feature; cardiac arrhythmia; electrocardiogram

摘要：Objective: Detecting atrial fibrillation (AF) from electrocardiogram (ECG) recordings remains a challenging task. In this paper, a new AF detection method was proposed to classify the ECG recordings into one of four classes: Normal rhythm, AF, Other rhythm, and Noisy recordings. Approach: The proposed method comprised preprocessing, feature extraction, and classification. In preprocessing, R-peaks were detected, and RR intervals and delta RR intervals were extracted. In feature extraction, 30 multi-level features were extracted, including AF features (n = 4), morphology features (n = 20), RR interval features (n = 2), and features of similarity index between beats (n = 4). In classification, these features were used to train an AdaBoosted decision tree ensemble for classifying ECG recordings into the four classes. The decision tree ensemble was trained with 100-fold cross-validation on the training dataset (n = 8528) provided by the 2017 PhysioNet/Computing in Cardiology (CinC) Challenge. Main results: The trained classifier was submitted to the Challenge for testing on the unavailable test dataset (n = 3658); the official F-1 scores for 'Normal', 'AF', 'Other' were 0.91, 0.82, and 0.73, respectively, and the overall F-1 score was 0.82 (ranking equal 5th with eight other algorithms in the 2017 PhysioNet/CinC Challenge). Significance: The proposed algorithm may be used as a new method for AF detection.