Non-Contact Atrial Fibrillation Detection Based on Video Pulse Features

被引：0

作者：

Zhang, Xu ^{[1
,2
]}

Yang, Xuezhi ^{[1
,2
]}

Liu, Xuenan ^{[1
,2
]}

Fang, Shuai ^{[2
]}

机构：

[1] School of Computer Science and Information Engineering, Hefei University of Technology, Hefei

[2] Anhui Key Laboratory of Industrial Safety and Emergency Technology, Hefei University of Technology, Hefei

来源：

Computer Engineering and Applications | 2023年 / 59卷 / 08期

关键词：

atrial fibrillation detection; facial video; feature selection; machine learning;

D O I：

10.3778/j.issn.1002-8331.2201-0385

中图分类号：

TN911 [通信理论];

学科分类号：

081002 ;

摘要：

Early detection of atrial fibrillation is very important for the prevention of cardiovascular and cerebrovascular diseases. This paper proposes a facial video atrial fibrillation detection method. The method extracts the pulse signals from the facial video by face tracking and improved complete ensemble empirical modes decomposition（ICEEMD）, and extracts atrial fibrillation discriminative features from video pulse signals according to the pulse characteristics during atrial fibrillation episodes. An improved recursive feature elimination feature selection method is designed to screen out the more important features for atrial fibrillation detection. Based on the above features, machine learning methods are used to achieve atrial fibrillation detection. Experiments are conducted on 122 cases of atrial fibrillation patients and 139 cases of normal sinus rhythm facial videos. The optimal features are the percentage of heartbeats with adjacent RR interval greater than 50 ms（PNN50）, the maximum value of RR intervals（maxRR）, and the horizontal radius of poincare diagram（SD2）etc. Based on the above optimal feature set, the accuracy of atrial fibrillation detection is 92.31%, the specificity is 90.24%, the sensitivity is 94.59%, and the AUC is 0.920 5. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：331 / 340

页数：9

共 53 条

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