基于腕部脉搏分析评估冠心病患者心功能的新方法。

A novel method for assessing cardiac function in patients with coronary heart disease based on wrist pulse analysis.

机构信息

Department of Basic Medical Science, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New District, Shanghai, 201203, China.

Institute of Intelligent Perception and Diagnosis, School of Mechanical and Power Engineering, East China University of Science and Technology, 130 Meilong Road, Xuhui District, Shanghai, 200237, China.

出版信息

Ir J Med Sci. 2023 Dec;192(6):2697-2706. doi: 10.1007/s11845-023-03341-6. Epub 2023 Mar 24.

DOI:10.1007/s11845-023-03341-6

PMID:36961673

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10692030/

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.

摘要

背景

及时评估 B 型利钠肽（BNP）标记冠心病（CHD）患者的慢性心力衰竭风险有助于降低患者死亡率。

目的

评估腕部脉搏信号在 CHD 患者心脏监测中的应用潜力。

方法

根据 BNP 水平，将 419 例 CHD 患者分为 1 组（BNP<95 pg/mL，n=249）、2 组（95<BNP<221 pg/mL，n=85）和 3 组（BNP>221 pg/mL，n=85）。非侵入性地测量腕部脉搏信号。分别采用时域法和多尺度熵（MSE）法提取脉搏特征。采用决策树（DT）和随机森林（RF）算法构建三组分类模型，并比较模型的性能指标。

结果

三组的脉搏特征差异显著，提示 CHD 患者心血管系统的病理状态不同。此外，RF 模型在性能指标上优于 DT 模型。此外，基于包含时域和 MSE 特征的数据集构建的最佳 RF 模型，其准确性、平均精度、平均召回率和平均 F1 评分分别为 90.900%、91.048%、90.900%和 90.897%。