Abstract
Abstract
Background
The 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.
Objective
To evaluate the potential of wrist pulse signals for use in the cardiac monitoring of patients with CHD.
Methods
A 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.
Results
The 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.
Conclusions
The wrist pulse detection technology employed in this study is useful for assessing the cardiac function of patients with CHD.
Funder
National Natural Science Foundation of China
Shanghai Science and Technology Committee Funding
Shanghai Key Laboratory of Health Identification and Assessment
Publisher
Springer Science and Business Media LLC
Cited by
1 articles.
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