JBiSE  Vol.7 No.4 , March 2014
Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine
ABSTRACT
Parkinson’s disease (PD) is the most common disease of motor system degeneration that occurs when the dopamine-producing cells are damaged in substantia nigra. To detect PD, various signals have been investigated, including EEG, gait and speech. Since approximately 90 percent of the people with PD suffer from speech disorders, speech analysis is considered as the most common technique for this aim. This paper proposes a new algorithm for diagnosing of Parkinson’s disease based on voice analysis. In the first step, genetic algorithm (GA) is undertaken for selecting optimized features from all extracted features. Afterwards a network based on support vector machine (SVM) is used for classification between healthy and people with Parkinson. The dataset of this research is composed of a range of biomedical voice signals from 31 people, 23 with Parkinson’s disease and 8 healthy people. The subjects were asked to pronounce letter “A” for 3 seconds. 22 linear and non-linear features were extracted from the signals that 14 features were based on F0 (fundamental frequency or pitch), jitter, shimmer and noise to harmonics ratio, which are main factors in voice signal. Because changing in these factors is noticeable for the people with PD, optimized features were selected among them. Of the various numbers of optimized features, the data classification was investigated. Results show that the classification accuracy percent of 94.50 per 4 optimized features, the accuracy percent of 93.66 per 7 optimized features and the accuracy percent of 94.22 per 9 optimized features, could be achieved. It can be observed that the best classification accuracy may be achieved using Fhi (Hz), Fho (Hz), jitter (RAP) and shimmer (APQ5).

Cite this paper
Shahbakhi, M. , Far, D. and Tahami, E. (2014) Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine. Journal of Biomedical Science and Engineering, 7, 147-156. doi: 10.4236/jbise.2014.74019.
References
[1]   Parkinson’s UK (2013) http://www.parkinsons.org.uk/about_parkinsons/what_is_parkinsons.aspx

[2]   National Parkinson Foundation (2013) http://www.parkinson.org/parkinson-s-disease.aspx

[3]   Webmd (2013) http://www.webmd.com/parkinsonsdisease/tc/parkinsons-disease-symptoms

[4]   Pezard, L., Jech, R. and RuÊzÏicÏka, E. (2001) Investigation of Non-Linear Properties of Multichannel EEG in the Early Stages of Parkinson’s Disease. Clinical Neurophysiology, 122, 38-45.

[5]   Ene, M. (2008) Neural Network-Based Approach to Discriminate Healthy People from Those with Parkinson’s Disease. Mathematics and Computer Science Series, 35, 112-116.

[6]   Little, M.A., McSharry, P.E., Hunter, E.J., Spielman, J. and Ramig, L.O. (2008) Suitability of Dysphonia Measurements for Telemonitoring of Parkinson’s Disease. IEEE Transactions on Biomedical Engineering, 56, 1015-1022.
http://dx.doi.org/10.1109/TBME.2008.2005954

[7]   Caglar, M.F., Cetisli, B. and Toprak, I.B. (2010) Automatic Recognition of Parkinson’s Disease from Sustained Phonation Tests Using ANN and Adaptive Neuro-Fuzzy Classifier. Journal of Engineering Science Design, 1, 59-64.

[8]   Gil, D. and Johnson, M. (2009) Diagnosing Parkinson by Using Artificial Neural Networks and Support Vector Machines. Global Journal of Compute Science and Technology, 9, 63-71.

[9]   Duffy, R.J. (2005) Motor Speech Disorders: Substrates, Differential Diagnosis and Management. 2nd Edition, Elsevier Mosby, St. Louis.

[10]   Ho, A.K., Iansek, R., Marigliani, C., Bradshaw, J.L. and Gates, S. (1998) Speech Impairment in a Large Sample of Patients with Parkinson’s Disease. Behaviour Neurology, 11, 131-137.
http://dx.doi.org/10.1155/1999/327643

[11]   Logemann, J.A., Fisher, H.B., Boshses, B. and Blonsky, E.R. (1978) Frequency and Co-Occurrence of Vocal-Tract Dysfunctions in Speech of a Large Sample of Parkinson Patients. Journal of Speech Hearing Disorder, 43, 47-57.

[12]   Sapir, S., Spielman, J.L., Ramig, L.O., Story, B.H. and Fox, C. (2007) Effects of Intensive Voice Treatment (the Lee Silverman Voice Treatment [LSVT]) on Vowel Articulation in Dysarthric Individuals with Idiopathic Parkinson Disease: Acoustic and Perceptual Findings. Journal of Speech Lang Hearing Research, 50, 899-912.
http://dx.doi.org/10.1044/1092-4388(2007/064)

[13]   Rahn, D.A., Chou, M., Jiang, J.J. and Zhang, Y. (2007) Phonatory Impairment in Parkinson’s Disease: Evidence from Nonlinear Dynamic Analysis and Perturbation Analysis. Journal of Voice, 21, 64-71.
http://dx.doi.org/10.1016/j.jvoice.2005.08.011

[14]   Ludwig, O., Nunes, U., Araujo, R., Schnitman, L. and Lepikson, H.A. (2009) Applications of Information Theory, Genetic Algorithms, and Neural Models to Predict Oil Flow. Communications in Nonlinear Science and Numerical Simulation, 14, 2870-2885.
http://dx.doi.org/10.1016/j.cnsns.2008.12.011

[15]   Nobel, W. (2006) What Is a Support Vector Machine? Nature Biotechnology, 24, 1565-1568.

[16]   Holland, J. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.

[17]   Goldberg, D. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, New York.

[18]   Jong, K.A. and Spears, W.M. (1992) A Formal Analysis of the Role of Multi-Point Crossover in Genetic Algorithms. Annals of Mathematics and Artificial Intelligence, 5, 1-26.
http://dx.doi.org/10.1007/BF01530777

[19]   Osowski, S., Siwek, K. and Markiewicz, T. (2004) MLP and SVM Networks—A Comparative Study. Proceedings of the 6th Nordic Signal Processing Symposium, Espoo, 37-40.

[20]   Ahmad, A.H., Viard-Gaudin, C., Khalid, M. and Poisson, E. (2004) Comparison of Support Vector Machine and Neural Network in Character Level Discriminant Training for Online Word Recognition, UNITEN Students Con on Research and Development, Malaisie.

[21]   Ganapathiraju, A., Hamaker, J. and Picone, J. (1998) Support Vector Machines for Speech Recognition. The Proceedings of the 5th International Conference on Spoken Language Processing, Incorporating the 7th Australian International Conference on Speech Science and Technology, Sydney.

[22]   Jean-Philippe, V., Tsuda, K. and Schölkopf, B. (2004) Kernel Methods in Computational Biology. MIT Press, Cambridge.

[23]   Center for Machine Learning and Intelligent Systems (2008)
http://archive.ics.uci.edu/ml/datasets/Parkinsons

[24]   Hastie, T., Tibshirani, R. and Friedman, J.H. (2001) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York.
http://dx.doi.org/10.1007/978-0-387-21606-5

 
 
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