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International Conference on Advances in Information Technology

IAIT 2010: Advances in Information Technology pp 183–191Cite as

Classification of Internal Carotid Artery Doppler Signals Using Hidden Markov Model and Wavelet Transform with Entropy

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 114))

Abstract

Doppler ultrasound has been usually preferred for investigation of the artery conditions in the last two decade, since it is a non-invasive method which is not risky. In this study, a biomedical system based on Discrete Hidden Markov Model (DHMM) has been developed in order to classify the internal carotid artery Doppler signals recorded from 191 subjects (136 of them had suffered from internal carotid artery stenosis and rest of them had been healthy subjects). Developed system comprises of three stages. In the first stage, for feature extraction, obtained Doppler signals were separated to its sub-bands using Discrete Wavelet Transform (DWT). In the second stage, entropy of each sub-band was calculated using Shannon entropy algorithm to reduce the dimensionality of the feature vectors via DWT. In the third stage, the reduced features of carotid artery Doppler signals were used as input patterns of the DHMM classifier. Our proposed method reached 97.38% classification accuracy with 5 fold cross validation (CV) technique. The classification results showed that purposed method is effective for classification of internal carotid artery Doppler signals.

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References

  1. Baker, W.H.: Diagnosis and Treatment of Carotid Artery Disease. Futura Publishing Company Inc., New York (1985)

    Google Scholar 

  2. Miranda, P., Lagares, A., Alen, J., Perez-Nunes, A., Arrese, I., Lobato, R.D.: Early transcranial Doppler after subarachnoid hemorrhage: clinical and radiological correlations. Surgical Neurology 65(3), 247–252 (2006)

    Article  Google Scholar 

  3. Özşen, S., Kara, S., Latifog̈lu, F., Güneş, S.: A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis. Computer Methods and Programs in Biomedicine 88, 246–255 (2007)

    Article  Google Scholar 

  4. Ceylan, M., Ceylan, R., Dirgenali, F., Özbay, Y.: Classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network. Computers in Biology and Medicine 37(1), 28–36 (2007)

    Article  Google Scholar 

  5. Özbay, Y., Ceylan, M.: Effects of window types on classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network. Computers in Biology and Medicine 37(3), 287–295 (2007)

    Article  Google Scholar 

  6. Polat, K., Kara, S., Latifog̈lu, F., Güneş, S.: Pattern detection of atherosclerosis from carotid artery Doppler signals using fuzzy weighted pre-processing and least square support vector machine (LSSVM). Ann. Biomed. Eng. 35(5), 724–732 (2007)

    Article  Google Scholar 

  7. Güler, İ., Übeyli, E.D.: A recurrent neural network classifier for Doppler ultrasound blood flow signals. Pattern Recognition Letters 27(1), 1560–1571 (2006)

    Article  Google Scholar 

  8. Güler, İ., Übeyli, E.D.: Implementing wavelet/probabilistic neural networks for Doppler ultrasound blood flow signals. Expert Systems with Applications 33(1), 162–170 (2007)

    Article  Google Scholar 

  9. Übeyli, E.D., Güler, İ.: Neural network analysis of internal carotid arterial Doppler signals: predictions of stenosis and occlusion. Expert Systems with Applications 25, 1–13 (2003)

    Article  Google Scholar 

  10. Subasi, A.: EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications 32(4), 1084–1093 (2007)

    Article  Google Scholar 

  11. Wu, J.D., Kuo, J.M.: An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network. Expert Systems with Applications 36(6), 9776–9783 (2009)

    Article  Google Scholar 

  12. Ocak, H.: Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Systems with Applications 36, 2027–2036 (2009)

    Article  Google Scholar 

  13. Purushotham, V., Narayanan, S., Suryanarayana, A.N.P.: Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition. NDT&E International 38(8), 654–664 (2005)

    Article  Google Scholar 

  14. Mallat, S.G.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Anal Machine Intelligence 11(7), 674–693 (1989)

    Article  MATH  Google Scholar 

  15. Tong, S., Bezerianos, A., Paul, J., Zhu, Y., Thakor, N.: Nonextensive entropy measure of EEG following brain injury from cardiac arrest. Physica A: Statistical Mechanics and its Applications 305(3-4), 619–628 (2002)

    Article  MATH  Google Scholar 

  16. Principe, J.C., Euliano, N.R., Lefebvre, W.C.: Neural and Adaptive Systems. John Wiley & Sons, New York (2000)

    Google Scholar 

  17. Turkoglu, I., Arslan, A., Ilkay, E.: An expert system for diagnosis of the heart valve diseases. Expert Systems with Applications 23, 229–236 (2002)

    Article  Google Scholar 

  18. Turkoglu, I., Arslan, A., Ilkay, E.: An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural Networks. Computers in Biology and Medicine 33, 319–331 (2003)

    Article  Google Scholar 

  19. Zhang, X.S., Roy, R.J.: Derived fuzzy knowledge model for estimating the depth of anesthesia. IEEE Transactions on Biomedical Engineering 48(3), 312–323 (2001)

    Article  Google Scholar 

  20. Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.K.: Entropies for detection of epilepsy in EEG. Computer Methods and programs in Biomedicine 80, 187–194 (2005)

    Article  Google Scholar 

  21. Yildiz, A., Akin, M., Poyraz, M., Kirbas, G.: Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction. Expert Systems with Applications 36, 7390–7399 (2009)

    Article  Google Scholar 

  22. Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. of the IEEE 77(2), 257–286 (1989)

    Article  Google Scholar 

  23. Kandaswamy, A., Kumar, C.S., Ramanathan, R.P., Jayaraman, S., Malmurugan, N.: Neural classification of lung sounds using wavelet coefficients. Computers in Biology and Medicine 34(6), 523–537 (2004)

    Article  Google Scholar 

  24. Kiymik, M.K., Akin, M., Subasi, A.: Automatic recognition of alertness level by using wavelet transform and artificial neural network. Journal of Neuroscience Methods 139, 231–240 (2004)

    Article  Google Scholar 

  25. Yildiz, A., Akin, M., Poyraz, M., Kirbas, G.: Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction. Expert Systems with Applications 36, 7390–7399 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Selçuk University, 42075, Konya, Turkey

    Harun Uğuz & Halife Kodaz

Authors
  1. Harun Uğuz
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  2. Halife Kodaz
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Editors and Affiliations

  1. School of Information Technology, King Mongkut’s University of Technology, Thonburi, 126 Pracha-U-Thit Rd., Bangmod, Thungkru, 10140, Bangkok, Thailand

    Borworn Papasratorn , Kittichai Lavangnananda , Wichian Chutimaskul  & Vajirasak Vanijja , ,  & 

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© 2010 Springer-Verlag Berlin Heidelberg

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Uğuz, H., Kodaz, H. (2010). Classification of Internal Carotid Artery Doppler Signals Using Hidden Markov Model and Wavelet Transform with Entropy. In: Papasratorn, B., Lavangnananda, K., Chutimaskul, W., Vanijja, V. (eds) Advances in Information Technology. IAIT 2010. Communications in Computer and Information Science, vol 114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16699-0_20

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  • DOI: https://doi.org/10.1007/978-3-642-16699-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16698-3

  • Online ISBN: 978-3-642-16699-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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