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American Journal of Biomedical Science and Engineering

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Neurological Disorder Detection Using Acoustic Features and SVM Classifier

American Journal of Biomedical Science and Engineering
Vol.1 , No. 5, Publication Date: Sep. 28, 2015, Page: 71-81

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Authors

[1]	Uma Rani K., Department of Biomedical Engineering and Research Centre, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India.
[2]	Mallikarjun S. Holi, Department of Electronics and Instrumentation Engineering, University B. D. T. College of Engineering, Visvesvaraya Technological University, Davangere, Karnataka, India.

Abstract

In neurological disordered patients, the physiological substrates necessary for the speech production may be altered and hence the acoustic properties may also change. The measurable information in the acoustic output of individual patients may provide valuable clues for diagnosing certain neurological diseases, course of disease progression, assessing response to medical treatment, or a combination of these. The various acoustic features can be extracted in time domain, frequency domain, time-frequency domain and by non linear feature methods and these features can be used for disordered voice detection. In the present work time domain features like pitch variation, jitter, shimmer, harmonic to noise ratio (HNR) and frequency domain features like Mel Frequency Cepstral Coefficients (MFCCs) are extracted from normal and neurological disordered subject’s voice signals. Both time domain and frequency domain features are given to a Support Vector Machine (SVM) classifier and the results are compared in detecting normal and neurological disordered subjects. It is observed that SVM classifier perform well for time domain features with a classification accuracy of 81.43% compared to the frequency domain features with classification accuracy of 71.43%.

Keywords

Neurological Disorder, Voice, MFCC, SVM

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