Modares Mechanical Engineering

Modares Mechanical Engineering

Mechanical pulse signal analysis in order to feature extraction to use in the diagnosis of CAD

Authors
Iran University of Science and Technology,tehran,iran
Abstract
Nowadays, diagnosis of diseases with high precision, high speed, low-cost and non-invasive approaches has become a necessity. In this regard, taking pulse signal is very easy and inexpensive, which due to the availability and feasibility of the process, can be very useful in the rapid diagnosis heart disease. If we can use the appropriate signal processing and intelligent methods in such a way that its accuracy and total cost equal those of other corresponding methods, we can say that we have reached a valuable achievement; in the current study we pursue the same purpose. In the first step, pressure pulse signals of 45 Coronary Arterial Disease (CAD) patients and 45 healthy persons are acquired from the left fingers using Task Force Monitor (TFM). Then the signals are filtered by wavelet transform (db6) and the wrong items are discarded. Then, the features corresponding to the CAD and healthy states are extracted which based on Time Domain Analysis. Finally, by choosing the best features, the data of healthy people and patients (CAD) are classified with Support Vector Machine (SVM) classifier by the accuracy rate of more than 85%.
Keywords

[1] H. Rezvani, An Overall Review on Traditional Chinese Medicine and Acupuncture, pp. 30-40, Tehran: Almoalla, 2015. (In Persian فارسی(
[2] L. Xu, M. Q.-H. Meng, K. Wang, W. Lu, N. Li, Pulse images recognition using fuzzy neural network, Expert Systems with Applications, Vol. 36, No. 2, pp. 3805-3811, 2009.
[3] J. Zhang, R. Wang, S. Lu, J. Gong, Z. Zhao, H. Chen, L. Cui, N. Wang, YYu, EasiCPRS: design and implementation of a portable Chinese pulsewave retrieval system, Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, New York: ACM, pp. 149-161, 2011.
[4] L. Xu, M. Q.-H. Meng, X. Qi, K. Wang, Morphology variability analysis of wrist pulse waveform for assessment of arteriosclerosis status, Journal of Medical Systems, Vol. 34, No. 3, pp. 331-339, 2010.
[5] Q. Y. Wu, Z. C. Ma, Y. N. Sun, Noninvasive power spectrum analysis of radial pressure waveform for assessment of vascular system, Journal of Mechanics in Medicine and Biology, Vol. 12, No. 01, pp. 125-138, 2012.
[6] C. M. Huang, C. C. Wei, Y. T. Liao, H. C. Chang, S. T. Kao, T. C. Li, Developing the effective method of spectral harmonic energy ratio to analyze the arterial pulse spectrum, Evidence-Based Complementary and Alternative Medicine, Vol. 2011, No. 1, pp. 1-9, 2011.
[7] Q. Wu, Power spectral analysis of wrist pulse signal in evaluating adult age, Intelligence Information Processing and Trusted Computing (IPTC), 2010 International Symposium on, New Jercy: IEEE, pp. 48-50, 2010.
[8] N. Garg, N. Babbar, Feature extraction of wrist pulse signals using gabor spectrogram, Indian Journal of Science and Technology, Vol. 9, No. 47, pp. 1-8, 2016.
[9] Y. Chen, L. Zhang, D. Zhang, D. Zhang, Computerized wrist pulse signal diagnosis using modified auto-regressive models, Journal of Medical Systems, Vol. 35, No. 3, pp. 321-328, 2011.
[10] J. J. Shu, Y. Sun, Developing classification indices for Chinese pulse diagnosis, Complementary Therapies in Medicine, Vol. 15, No. 3, pp. 190- 198, 2007.
[11] 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, No. 10, pp. 1283-1289, 2009.
[12] H. T. Wu, C. H. Lee, C. K. Sun, J. T. Hsu, R. M. Huang, C. J. Tang, Arterial waveforms measured at the wrist as indicators of diabetic endothelial dysfunction in the elderly, IEEE Transactions on Instrumentation and Measurement, Vol. 61, No. 1, pp. 162-169, 2012.
[13] R. Guo, Y. Wang, H. Yan, J. Yan, F. Yuan, Z. Xu, G. Liu, W. Xu, Analysis and recognition of traditional Chinese medicine pulse based on the hilberthuang transform and random forest in patients with coronary heart disease, Evidence-Based Complementary and Alternative Medicine, Vol. 2015, No. 1, pp. 1-8, 2015.
[14] L. Zhang, W. Yang, D. Zhang, Wrist-pulse signal diagnosis using ICpulse, Bioinformatics and Biomedical Engineering, 2009. ICBBE 2009. 3rd International Conference on, New Jercy: IEEE, pp. 1-4, 2009.
[15] D. Rangaprakash, D. N. Dutt, Study of wrist pulse signals using time domain spatial features, Computers & Electrical Engineering, Vol. 45, No. 1, pp. 100-107, 2015.
[16] D. Meyer, F. Leisch, K. Hornik, The support vector machine under test, Neurocomputing, Vol. 55, No. 1, pp. 169-186, 2003.
[17] J. Fortin, G. Haitchi, A. Bojic, W. Habenbacher, R. Grullenberger, A. Heller, R. Pacher, P. Wach, F. Skrabal, Validation and verification of the Task Force Monitor, Results of Clinical Studies for FDA, Vol. 510, No. 1, pp. 1-7, 2001.
[18] J. Fortin, T. Klinger, C. Wagner, H. Sterner, C. Madritsch, R. Grüllenberger, A. Hacker, W. Habenbacher, F. Skrabal, The Task Force Monitor—a non-invasive beat-to-beat monitor for hemodynamic and autonomic function of the human body, Proceedings of the 20th annual International Conference of the IEEE Engineering in Medicine and Biology Society, New Jercy: IEEE, pp. 1-8, 1998.
[19] K. Wang, L. Xu, L. Wang, Z. Li, Y. Li, Pulse baseline wander removal using wavelet approximation, in Computers in Cardiology, New Jercy: IEEE, pp. 605-608, 2003.
[20] J. Esmaeilpour, S. Mirzakoochaki, Classification of cardiac arrhythmias by learning vector quantizater network and based on the extracted features from the wavelet transformation, Iranian Journal of Biomedical Engineering, Vol. 1, No. 3, pp. 167-176, 2007. (in Persian فارسی(
[21] R. Soleymani, M. Rouhani, Heart arrhythmia diagnosis by neural networks using chaotic features of HRV signal and generalized discriminant analysis, Iranian Journal of Biomedical Engineering, Vol. 5, No. 1, pp. 89-104, 2011. (in Persian فارسی(