ELECTRICA

A New Approach for Detection of Pathological Voice Disorders with Reduced Parameters

1.

Biomedical Equipment Technology, Başkent University Vocational School of Technical Sciences, Ankara, Turkey

ELECTRICA 2018; 18: 60-71
DOI: 10.5152/iujeee.2018.1810
Read: 541 Downloads: 140 Published: 23 February 2018

Voice data hasdemonstrated chaotic behavior in previous studies. Therefore, studying thelinear properties alone does not yield successful results. This is valid forthe examination of voice data as well. Therefore, conducting studies includingchaotic features as well as existing technologies is inevitable. The mainpurpose of this study is to detect voice pathologies with fewer specialfeatures using new chaotic features. Both linear and nonlinear characteristicswere used in this study. In this context, the largest Lyapunov exponents andentropy are preferred as chaotic properties because of their success inprevious studies. Very few results with 100% accuracy were obtained in theexperimental studies. In this study, multiple support vector machines (SVMs)were selected as a classifier because of their success in previous similar datatypes. Thus, the desired accuracy level was achieved using fewer features.Resultantly, the process complexity decreased and the system speed increased.

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EISSN 2619-9831