Analysis on pulse features of coronary heart disease patients with or without a history of ischemic stroke

冠心病伴或不伴缺血性卒中病史患者的脉象特征分析

Objective To evaluate the capability of wrist pulse analysis in distinguishing three physiolog-ical and pathological conditions:healthy individuals,coronary heart disease(CHD)patients without a history of ischemic stroke,and CHD patients with a history of ischemic stroke.Methods Study participants were recruited from Shuguang East Hospital,Yueyang Hospital of Integrated Traditional Chinese and Western Medicine,and Shanghai Municipal Hospital of Traditional Chinese Medicine,affiliated with Shanghai University of Traditional Chinese Medicine,from April 15 to September 15,2021.They were categorized into three groups:healthy controls(Group 1),CHD patients without a history of ischemic stroke(Group 2),and CHD patients with a history of ischemic stroke(Group 3).The wrist pulse signals of the study participants were non-invasively collected using a pulse diagnosis instrument.The linear time-domain features and nonlinear time-series multiscale entropy(MSE)features of the pulse signals were extracted using time-domain analysis and the MSE methods,which were subsequently compared between groups.Based on these extracted features,a recognition model was developed using a random forest(RF)algorithm.The classification performance of the models was evaluated using metrics,including accuracy,precision,recall,and F1-score derived from confusion matrix as well as the area under the receiver operating characteristics(ROC)curve(AUC).Results A total of 189 participants were enrolled,with 63 in Group 1,61 in Group 2,and 65 in Group 3.Compared with Group 1,Group 2 showed significant increases in pulse features H2/H1,H3/H1,W1,W2,and W2/T,and decreased in MSE_(1)-MSE7(P<0.05),while Group 3 showed significant increases in pulse features T5/T4,T,H1/T1,W1,W2,AS,and Ad,and de-creased in MSE_(1)-MSE_(20)(P<0.05).Compared with Group 2,Group 3 demonstrated notable increases in H1/T1 and As(P<0.05).The RF model achieved precision of 80.00%,61.54%,and 61.54%,recall of 74.29%,60.00%,and 68.97%,F1-scores of 70.04%,60.76%,and 65.04%,and AUC values of 0.92,0.74,and 0.81 for Groups 1,2,and 3,respectively.The overall accuracy was 67.69%,with micro-average AUC of 0.83 and macro-average AUC of 0.82.Conclusion Differences in pulse features reflect variations in arterial compliance,peripheral resistance,cardiac afterload,and pulse signal complexity among healthy individuals,CHD patients without a history of ischemic stroke,and those with such a history.The developed pulse-based recognition model holds the potential in distinguishing between these three groups,offering a novel diagnostic reference for clinical practice.

目的评估脉图分析技术在识别健康人群、冠心病(CHD)患者不伴以及伴缺血性卒中病史的三类不同生理病理状态人群的应用潜力。方法于2021年4月15日至9月15日在上海中医药大学附属曙光医院东院、岳阳中西医结合医院以及上海市中医医院招募研究对象,并将他们分为三组:健康对照组(组1)、无缺血性卒中史的CHD患者(组2)和有缺血性卒中史的CHD患者(组3)。应用脉诊仪无创采集脉象信号,运用时域分析和多尺度熵(MSE)方法提取脉象信号的线性时域特征和非线性时间序列MSE特征,并进行组间比较分析。基于这些脉象特征,运用随机森林(RF)算法建立识别模型。采用混淆矩阵计算的准确率、精确率、召回率、F1分数以及受试者工作特征曲线下(ROC)面积(AUC)等指标评估模型的分类性能。结果最终纳入189名受试者,其中组1共63例,组2共61例,组3共65例。与组1相比,组2脉象特征H2/H1、H3/H1、W1、W2和W2/T均显著升高,其MSE_(1)-MSE7显著降低(P<0.05),组3脉象特征T5/T4、T、H1/T1、W1、W2、AS和Ad均显著升高,其MSE_(1)-MSE_(20)显著降低(P<0.05);与组2相比,组3的H1/T1和As值显著升高(P<0.05)。RF模型对组1、2、3的识别精确率分别为80.00%、61.54%、61.54%,召回率分别为74.29%、60.00%、68.97%,F1值分别为70.04%、60.76%、65.04%,AUC值分别为0.92、0.74、0.81。模型总体准确率为67.69%,微观平均AUC为0.83,宏观平均AUC为0.82。结论脉象特征差异体现了健康人群、无缺血性卒中病史的CHD患者以及有缺血性卒中病史的CHD患者在动脉顺应性、外周阻力、心脏后负荷以及脉象信号系统复杂性存在的差异。基于脉象的识别模型在区分这三类人群上展现了良好潜力,有望为临床实践提供新的参考依据。