» Articles » PMID: 26162371

Association of Hypertension with Physical Factors of Wrist Pulse Waves Using a Computational Approach: a Pilot Study

Overview
Publisher Biomed Central
Date 2015 Jul 12
PMID 26162371
Citations 9
Authors
Affiliations
Soon will be listed here.
Abstract

Background: The objectives of this pilot study were to examine the association between hypertension and physical factors of wrist pulse waves to avoid subjective diagnoses in Traditional Chinese Medicine (TCM) and Traditional Korean Medicine (TKM). An additional objective was to assess the predictive power of individual and combined physical factors in order to identify the degree of agreement between diagnosis accuracies using physical factors and using a sphygmomanometer in the prediction of hypertension.

Methods: In total, 393 women aged 46 to 73 years participated in this study. Logistic regression (LR) and a naïve Bayes algorithm (NB) were used to assess statistically significant differences and the predictive power of hypertension, and a wrapper-based machine learning method was used to evaluate the predictive power of combinations of physical factors.

Results: In both wrists, L-PPI and R-PPI (maximum pulse amplitudes in the left Gwan and right Gwan) were the factors most strongly associated with hypertension after adjusting for age and body mass index (p = <0.001, odds ratio (OR) = 2.006 on the left and p = <0.001, OR = 2.504 on the right), and the best predictors (NB-AUC = 0.692, LR-AUC = 0.7 on the left and NB-AUC = 0.759, LR-AUC = 0.763 on the right). Analyses of both individual and combined physical factors revealed that the predictive power of the physical factors in the right wrist was higher than for the left wrist. The predictive powers of the combined physical factors were higher than those of the best single predictors in both the left and right wrists.

Conclusion: We suggested new physical factors related to the sum of the area on the particular region of pulse waves in both wrists. L-PPI and R-PPI among all variables used in this study were good indicators of hypertension. Our findings support the quantification and objectification of pulse patterns and disease in TCM and TKM for complementary and alternative medicine.

Citing Articles

Uncovering the scientific landscape: A bibliometric and Visualized Analysis of artificial intelligence in Traditional Chinese Medicine.

Cao S, Wei Y, Yue Y, Wang D, Xiong A, Yang J Heliyon. 2024; 10(18):e37439.

PMID: 39315188 PMC: 11417164. DOI: 10.1016/j.heliyon.2024.e37439.


Visualizing a Cold Stress-Specific Pulse Wave in Traditional Pulse Diagnosis ('Tight Pulse') Correlated with Vascular Changes in the Radial Artery Induced by a Cold Pressor Trial.

Song J, Choi J, Lee B, Eom D, Song C Sensors (Basel). 2024; 24(7).

PMID: 38610298 PMC: 11014190. DOI: 10.3390/s24072086.


Machine learning classification of polycystic ovary syndrome based on radial pulse wave analysis.

Lim J, Li J, Feng X, Feng L, Xia Y, Xiao X BMC Complement Med Ther. 2023; 23(1):409.

PMID: 37957660 PMC: 10644435. DOI: 10.1186/s12906-023-04249-5.


Machine learning in TCM with natural products and molecules: current status and future perspectives.

Ma S, Liu J, Li W, Liu Y, Hui X, Qu P Chin Med. 2023; 18(1):43.

PMID: 37076902 PMC: 10116715. DOI: 10.1186/s13020-023-00741-9.


Radial Pulse Wave Signals Combined with Ba-PWV for the Risk Prediction of Hypertension and the Monitoring of Its Accompanying Metabolic Risk Factors.

Qi Z, Zhao Z, Xu J, Zhu L, Zhang Y, Bao Y Evid Based Complement Alternat Med. 2020; 2020:3926851.

PMID: 32419802 PMC: 7210560. DOI: 10.1155/2020/3926851.


References
1.
King E, Cobbin D, Ryan D . The reliable measurement of radial pulse: gender differences in pulse profiles. Acupunct Med. 2003; 20(4):160-7. DOI: 10.1136/aim.20.4.160. View

2.
Chen Y, Zhang L, Zhang D, Zhang D . Wrist pulse signal diagnosis using modified Gaussian models and Fuzzy C-Means classification. Med Eng Phys. 2009; 31(10):1283-9. DOI: 10.1016/j.medengphy.2009.08.008. View

3.
Lee B, Ku B, Park K, Kim K, Kim J . A new method of diagnosing constitutional types based on vocal and facial features for personalized medicine. J Biomed Biotechnol. 2012; 2012:818607. PMC: 3415144. DOI: 10.1155/2012/818607. View

4.
Kim H, Kim J, Park Y, Park Y . Development of pulse diagnostic devices in Korea. Integr Med Res. 2017; 2(1):7-17. PMC: 5481688. DOI: 10.1016/j.imr.2013.01.003. View

5.
Lin W, Chen J . Class-imbalanced classifiers for high-dimensional data. Brief Bioinform. 2012; 14(1):13-26. DOI: 10.1093/bib/bbs006. View