文献类型：期刊文献

英文题名：Bradycardia and Tachycardia Detection Using a Synthesis-by-Analysis Modeling Approach of Pulsatile Signal

作者：Chou, Yongxin[1,4];Gu, Jason[2,5];Liu, Jicheng[1];Gu, Ya[1];Lin, Jiajun[3,4]

机构：[1]Changshu Inst Technol, Sch Elect & Automat Engn, Suzhou 215500, Peoples R China;[2]Dalhousie Univ, Dept Elect & Comp Engn, Halifax, NS B3H 4R2, Canada;[3]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[4]East China Univ Sci & Technol, Res Inst Changshu Co Ltd, Suzhou 215500, Peoples R China;[5]Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730000, Gansu, Peoples R China

年份：2019

卷号：7

起止页码：131256

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20200308053865);WOS:【SCI-EXPANDED(收录号:WOS:000498617900002)】；

基金：This work was supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20170436 and Grant BK20181033, in part by the Jiangsu Postdoctoral Research Project, in part by the State Scholarship Fund Organized by China Scholarship Council, in part by the National Natural Science Foundation of China under Grant 61901062 and Grant 61903050, and in part by the National Science and Engineering Research Council of Canada.

语种：英文

外文关键词：Synthesis-by-analysis modeling; pulsatile signal; bradycardia and tachycardia detection; random forest

摘要：Bradycardia and tachycardia reflect abnormalities of the heart that can lead to severe harm to the cardiovascular system. The pulsatile signal is a useful tool to detect these two kinds of arrhythmias. In this study, we present a pulsatile synthesis-by-analysis (PSA) modeling based method to detect bradycardia and tachycardia. A new PSA modeling method was proposed to quantitively describe the changes of pulsatile waves, and we obtained twelve parameters for constructing a feature vector from the PSA model of each wave, by which we trained classifiers of probabilistic neural network (PNN) and random forest (RF). Our experiments were performed on the Fantasia and 2015 PhysioNet/CinC Challenge databases. Some pathological and physiological changes were extracted from the average models of the subjects in different groups. The two-sample ks-test results show that all the parameters between different groups are all markedly different (h = 1, p < 0.05). The classification results show that the performances of RF classifiers are better than that of PNN. The kappa coefficients (KC) of RF classifiers are all over 97%, and that of the classifying among bradycardia, tachycardia, and healthy subjects is 98.652 +/- 0.217%. Compared with the performance of some former methods, the obtained results demonstrate that the presented method promotes the classification performance remarkably and has the potential to diagnose bradycardia and tachycardia in m-health.