文献类型：期刊文献

英文题名：A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis

作者：Wu, Wen-jie[1];Chen, Rui[1];Guo, Rui[1];Yan, Jian-jun[2];Zhang, Chun-ke[1];Wang, Yi-qin[1];Yan, Hai-xia[1];Zhang, Ye-qing[3]

机构：[1]Shanghai Univ Tradit Chinese Med, Dept Basic Med Sci, 1200 Cailun Rd, Shanghai 201203, Peoples R China;[2]East China Univ Sci & Technol, Inst Intelligent Percept & Diag, Sch Mech & Power Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China;[3]Shanghai Univ Tradit Chinese Med, Shanghai Municipal Hosp Tradit Chinese Med, Dept Chinese Internal Med, Shanghai 200071, Peoples R China

年份：2023

卷号：192

期号：6

起止页码：2697

外文期刊名：IRISH JOURNAL OF MEDICAL SCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000983487300001)】；

基金：National Natural Science Foundation of China (No.82074332) and Shanghai Science and Technology Committee Funding (No.19441901100), and Shanghai Key Laboratory of Health Identification and Assessment (No. 21DZ2271000).

语种：英文

外文关键词：B-type natriuretic peptide; Chronic heart failure; Coronary heart disease; Machine learning algorithm; Multiscale entropy method; Time-domain method; Wrist pulse signal

摘要：BackgroundThe timely assessment of B-type natriuretic peptide (BNP) marking chronic heart failure risk in patients with coronary heart disease (CHD) helps to reduce patients' mortality.ObjectiveTo evaluate the potential of wrist pulse signals for use in the cardiac monitoring of patients with CHD.MethodsA total of 419 patients with CHD were assigned to Group 1 (BNP < 95 pg/mL, n = 249), 2 (95 < BNP < 221 pg/mL, n = 85), and 3 (BNP > 221 pg/mL, n = 85) according to BNP levels. Wrist pulse signals were measured noninvasively. Both the time-domain method and multiscale entropy (MSE) method were used to extract pulse features. Decision tree (DT) and random forest (RF) algorithms were employed to construct models for classifying three groups, and the models' performance metrics were compared.ResultsThe pulse features of the three groups differed significantly, suggesting different pathological states of the cardiovascular system in patients with CHD. Moreover, the RF models outperformed the DT models in performance metrics. Furthermore, the optimal RF model was that based on a dataset comprising both time-domain and MSE features, achieving accuracy, average precision, average recall, and average F1-score of 90.900%, 91.048%, 90.900%, and 90.897%, respectively.ConclusionsThe wrist pulse detection technology employed in this study is useful for assessing the cardiac function of patients with CHD.