Random Articles
Reseach Article
Support Vector Machine for Abnormal Pulse Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 22 - Number 7 |
| Year of Publication: 2011 |
| Authors: Bhaskar Thakker, Anoop Lal Vyas |
10.5120/2597-3610
|
Bhaskar Thakker, Anoop Lal Vyas . Support Vector Machine for Abnormal Pulse Classification. International Journal of Computer Applications. 22, 7 ( May 2011), 13-19. DOI=10.5120/2597-3610
Abstract
Radial pulse signals have been utilized in ancient culture for the health diagnosis due to its simple, non invasive and effective approach. Characteristics of a newly identified abnormal pulse in the subjects suffering from gastritis and arthritis are discussed along with commonly visible healthy pulse patterns in this work. A binary classifier to segregate such abnormal pulses from healthy pulse patterns is modeled using linear, quadratic as well as support vector machine based algorithms. Frequency domain features derived from power spectral density of the pulse signal are ranked to achieve dimensionality reduction. It has been found that the support vector machine with linear kernel classifies the abnormal pulse signals with highest success rate of 99.2% utilizing only two ranked frequency domain features.
References
- B. Flaws “The Secrets of Chinese Pulse Diagnosis”, 1995 Blue Poppy Press, Boulder, CO.
- Wang, Shu-ho., “The Pulse Classic”, 1997 Blue Poppy Press, Boulder, CO.
- V. Dattatray Lad “Secrets of the Pulse the Ancient Art of Ayurvedic Pulse Diagnosis” Motilal Banarasidas Publishers INDIA, 2005.
- S. Upadhyaya “Nadi Vijyaya Ancient Pulse Science” Chaukhamba Publishers, INDIA 2005.
- Jianjun Yan, Yiqin Wang, Fufeng Li, Haixia Yan, Chunming Xia, Rui Guo “Analysis and Classification of Wrist Pulse using Sample Entropy”, Proceedings of IEEE International Symposium on IT in Medicine and Education, 2008 pp. 609 – 612.
- L S Xu, K Q Wang, L Wang, “Pulse waveforms Classification Based on Wavelet Network” Proceeding of IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, china, 2005 pp. 4596-4599.
- L U Wang, Kuan-Quan Wang, Li-Wheng Xu “Recognizing Wrist Pulse Waveforms with Improved Dynamic Time Warping Algorithm” Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 2004; pp. 3644-3699.
- L S Xu, Max Q H Meng, K Q Wang “Pulse Image Recognition Using Fuzzy Neural Network” Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology, France, 2007; pp.3148 - 3151.
- Bhaskar Thakker, Anoop Lal Vyas “Radial Pulse Analysis at Deep Pressure in Abnormal Health Conditions” Third International Conference on BioMedical Engineering and Informatics, October, 2010; pp. 1007-1010.
- Gordon A. Ewy, Jorge C. Rios and Frank I. Marcus “The Dicrotic Arterial Pulse” Circulation: Journal of American Heart Association, Vol. 39, May 1969, pp. 655 – 661.
- Richard O. Duda, Peter E. Hart, David G. Stork Pattern Classification. Second edition Wiley, 2006.
- C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
- Asa Ben-Hur, Cheng Soon Ong, Soren Sonnenburg, Bernhard Scholkopf and Gunnar Ratsch, “Support Vector Machines and Kernels for Computational Biology” PLOS Computational Biology Vol. 4, October 2008.
- D. Alvarez-Estevez, V. Moret-Bonillo, “Identification of Electroencephalographic Arousals in Multichannel Sleep Recordings”, IEEE Transactions on Biomedical Engineering, Vol. 58, No. 1, January 2011, pp. 54 – 63.
- B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,” in Proc. 5th Annu. ACM Workshop Computational Learning Theory (COLT), Pittsburgh, PA, 1992, pp. 144–152.
- V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
Index Terms
Keywords