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 str...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.展开更多
During the test on transient pressure signal in explosion field,false trigger caused by field interference can lead to test failure.To improve the stability of test system,a signal detection and recognition technology...During the test on transient pressure signal in explosion field,false trigger caused by field interference can lead to test failure.To improve the stability of test system,a signal detection and recognition technology is proposed for transient pressure test system.In the process of signal acquisition,firstly,electrical levels are monitored in real time to find effective abrupt changes and mark them;then the effective data segments are detecdted totected;thus the effective signals can be acquired in turn finally.The experimental results show that the shock wave signal can be collected effectively and the reliability of the test system can be improved after removal of interferences.展开更多
Being aimed at the weakness of short range target′s threshold value recognition system,the double passage And Gate recognition system was put forward on the correlativity of target signals and randomness of noise ...Being aimed at the weakness of short range target′s threshold value recognition system,the double passage And Gate recognition system was put forward on the correlativity of target signals and randomness of noise signals Through state analysis and inference of state transition probability,both the reliability and early burst probability of the system were obtained in theory展开更多
基金National Natural Science Foundation of China(82074332)Shanghai Key Laboratory of Health Identification and Assessment(21DZ2271000)the 14th Batch of Science and Innovation Program for Undergraduates(202110268031).
文摘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.
基金The 11th Postgraduate Technology Innovation Project of North University of China(No.20141142)
文摘During the test on transient pressure signal in explosion field,false trigger caused by field interference can lead to test failure.To improve the stability of test system,a signal detection and recognition technology is proposed for transient pressure test system.In the process of signal acquisition,firstly,electrical levels are monitored in real time to find effective abrupt changes and mark them;then the effective data segments are detecdted totected;thus the effective signals can be acquired in turn finally.The experimental results show that the shock wave signal can be collected effectively and the reliability of the test system can be improved after removal of interferences.
文摘Being aimed at the weakness of short range target′s threshold value recognition system,the double passage And Gate recognition system was put forward on the correlativity of target signals and randomness of noise signals Through state analysis and inference of state transition probability,both the reliability and early burst probability of the system were obtained in theory
