Analysis on pulse features of coronary heart disease patients with or without a history of ischemic stroke
Objective: To evaluate the capability of wrist pulse analysis in distinguishing three physiological 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 partici...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-09-01
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| Series: | Digital Chinese Medicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589377724000624 |
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| _version_ | 1846099023467905024 |
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| author | Li Xin Li Wei Ng Man-In Parry Natalie Ann Li Siqi Li Rui Guo Rui |
| author_facet | Li Xin Li Wei Ng Man-In Parry Natalie Ann Li Siqi Li Rui Guo Rui |
| author_sort | Li Xin |
| collection | DOAJ |
| description | Objective: To evaluate the capability of wrist pulse analysis in distinguishing three physiological 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 MSE1 – MSE7 (P < 0.05), while Group 3 showed significant increases in pulse features T5/T4, T, H1/T1, W1, W2, AS, and Ad, and decreased in MSE1 – MSE20 (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. |
| format | Article |
| id | doaj-art-a7f6ea6d38f04eb6abb85d84075e85a0 |
| institution | Kabale University |
| issn | 2589-3777 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Chinese Medicine |
| spelling | doaj-art-a7f6ea6d38f04eb6abb85d84075e85a02025-01-01T05:11:06ZengKeAi Communications Co., Ltd.Digital Chinese Medicine2589-37772024-09-0173264273Analysis on pulse features of coronary heart disease patients with or without a history of ischemic strokeLi Xin0Li Wei1Ng Man-In2Parry Natalie Ann3Li Siqi4Li Rui5Guo Rui6School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaSchool of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaSchool of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaSchool of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaSchool of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaSchool of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaCorresponding author:; School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaObjective: To evaluate the capability of wrist pulse analysis in distinguishing three physiological 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 MSE1 – MSE7 (P < 0.05), while Group 3 showed significant increases in pulse features T5/T4, T, H1/T1, W1, W2, AS, and Ad, and decreased in MSE1 – MSE20 (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.http://www.sciencedirect.com/science/article/pii/S2589377724000624Pulse diagnosisCoronary heart disease (CHD)Ischemic strokeSignal processingPattern recognition |
| spellingShingle | Li Xin Li Wei Ng Man-In Parry Natalie Ann Li Siqi Li Rui Guo Rui Analysis on pulse features of coronary heart disease patients with or without a history of ischemic stroke Digital Chinese Medicine Pulse diagnosis Coronary heart disease (CHD) Ischemic stroke Signal processing Pattern recognition |
| title | Analysis on pulse features of coronary heart disease patients with or without a history of ischemic stroke |
| title_full | Analysis on pulse features of coronary heart disease patients with or without a history of ischemic stroke |
| title_fullStr | Analysis on pulse features of coronary heart disease patients with or without a history of ischemic stroke |
| title_full_unstemmed | Analysis on pulse features of coronary heart disease patients with or without a history of ischemic stroke |
| title_short | Analysis on pulse features of coronary heart disease patients with or without a history of ischemic stroke |
| title_sort | analysis on pulse features of coronary heart disease patients with or without a history of ischemic stroke |
| topic | Pulse diagnosis Coronary heart disease (CHD) Ischemic stroke Signal processing Pattern recognition |
| url | http://www.sciencedirect.com/science/article/pii/S2589377724000624 |
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