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http://dx.doi.org/10.5573/ieie.2014.51.1.185

Classification Model of Chronic Gastritis According to The Feature Extraction Method of Radial Artery Pulse Signal  

Choi, Sang-Ho (Sungkyunkwan University)
Shin, Ki-Young (Korea Electrotechnology Research Institute)
Kim, Jeauk (Korea Institute of Oriental Medicine)
Jin, Seung-Oh (Korea Electrotechnology Research Institute)
Lee, Tea-Bum (Korea Electrotechnology Research Institute)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.51, no.1, 2014 , pp. 185-194 More about this Journal
Abstract
One in every 10 persons suffer from chronic gastritis in Korea. Endoscopy is most commonly used to diagnose the chronic gastritis. Endoscopic diagnosis is precise but it is accompanied with pain and high cost. According to pulse diagnosis in Traditional East Asian Medicine, health problems in stomach can be diagnosed with radial pulse signals in 'Guan' location in the right wrist, which are non-invasive and cost-effective. In this study, we developed a classification model of chronic gastritis using pulse signals in right 'Guan' location. We used both linear discrimination method and logistic regression model with respect to pulse features obtained with a peak-valley detection algorithm and a Gaussian model. As a result, we obtained sensitivity ranged between 77%~89% and specificity ranged between 72%~83% depending on classification models and feature extraction methods, and the average classification rates were approximately 80%, irrespective of the models. Specifically, the Gaussian model were featured by superior sensitivities (89.1% and 87.5%) while the peak-valley detection method showed superior specificities (82.8% and 81.3%), and the average classification rate (sensitivity + specificity) of the Gaussian model was 80.9% which was 1.2% ahead of the peak-valley method. In conclusion, we obtained a reliable classification model for the chronic gastritis based on the radial pulse feature extraction algorithms, where the Gaussian model was featured by outperformed sensitivity and the peak-valley method was featured by outperformed specificity.
Keywords
Chronic gastritis; Pulse diagnosis; Radial pulse; logistic regression model;
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  • Reference
1 Y.G. Lee, Diagnostics Atlas III, Seoul, South Korea: CHUNGDAM, pp.11-14, 2003.
2 B. Flaws, The Secret of Chinese Pulse Diagnosis, Boulder, CO: Blue Poppy Press, pp. 4-8, 1995.
3 H. L. Lee, S. Suzuki, Y. Adachi, M. Umeno and Shan K, "Fuzzy Theory in Traditional Chinese Pulse Diagnosis," Proceeding of International Joint Conference on Neural Networks, pp. 774-777, Nagoya, Japan, Oct. 1993.
4 Y. Z. Yoon, M. H. Lee and K. S. Soh, "Pulse Type Classification by Varying Contact Pressure," IEEE Engineering in Medicine and Biology Magazine, vol. 19, pp.106-110, Nov/Dec. 2000.
5 S. E. Fu, S. P. Lai, "A system for pulse measurement and analysis of Chinese medicine," Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1695-1696, Seattle, WA, USA, Nov. 1989.
6 Lisheng Xu, Max Q.-H. Meng, Kuanquan Wang, Wang Lu, Naimin Li, "Pulse images recognition using fuzzy neural network," Expert Systems with Applications, vol. 36, pp. 3805-3811, March 2009.   DOI   ScienceOn
7 Yan Haixia, Wang Yiqin, Liu Zhaorong, Guo Rui, Li Fufeng, Run Fengying, Hong Yujian, "Feature Extraction for Pulse Waveform in Traditional Chinese Medicine by Hemodynamic Analysis," IEEE International Conference on Bioinformatics and Biomedicine, pp. 234-238, Washington, D.C, USA, Nov. 2009.
8 B. Thakker, A.L.Vyas, O.Farooq, D.Mulvaney, S. Datta, "Wrist pulse signal classification for health diagnosis," Biomedical Engineering and Informatics 4th International Conference, pp.1799-1805, Shanghai, China, Oct. 2011.
9 K.Y.Shin, T.B.Lee, S.O.Jin, S.H.Choi, S.K.Yoo, Y.Huh, J.U.Kim, J.Y.Kim, "Characteristics of the pulse wave in patients with chronic gastritis and the health in korean medicine," 34th annual international IEEE EMBS conference, pp. 992-995, San Diego, USA, August 28-September 1, 2012.
10 S. H. Choi, K. Y. Shin, J. T. Shin, "Classification method of chronic gastritis by modeling of pulse signal," The Korea Institute of Information, Electronics and Communication Technology, vol. 5, no. 3, pp. 144-151. Sep. 2012.
11 Y. Zavros, K. A. Eaton, W. Kang, S. Rathinavelu, V. Katukuri, J. Y. Kao, L. C. Samuelson, J. L. Merchant, "Chornic gastritis in the hypochlorhydric gastrin-deficient mouse progresses to adenocarcinoma," Oncogene, vol. 24, pp. 2354-2366, March 2005.   DOI   ScienceOn
12 S. Walsh, E. King, Pulse diagnosis: a clinical guide, Elsevier Health Sciences, 2007.
13 Y. Chen, L. Zhang, D. Zhang, D. Zhang, "Wrist pulse signal diagnosis using modified Gaussian models and fuzzy c-means classification," Medical engineering & physics, vol. 31, pp. 1283-1289, Dec. 2009.   DOI   ScienceOn
14 J. Zhang, R. Wang, S. Lu, J. Gong, Z. Zhao, H. Chen, L. Cui, N. Wang, and Y. Yu, "EasiCPRS: design and implementation of a portable Chinese pulse-wave retrieval system," SenSys, pp. 149-161, 2011.
15 Lisheng Xu, Max Q.-H. Meng, Kuanquan Wang, Wang Lu, Naimin Li, "Pulse images recognition using fuzzy neural network," Conf Proc IEEE Enq Med Biol Soc, pp. 3148-3151, 2007.
16 J. J. Shu, Y. Sun, "Developing classification indices for Chinese pulse diagnosis," Complementary therapies in medicine, vol. 15, pp.190-198, Sept. 2007.   DOI   ScienceOn
17 Hastie, T., Tibshirani, R. and Friedman, J., The Elements of statistical Learning, Springer, New York, 2001.
18 B.H.Yang, Understanding Multivariate Data Analysis, Communicationbooks, Korea, 2006.