title: Nonlinear analysis and classification of vocal disorders.
abstract: This paper suggests a way to investigate pathological voice signals from nonlinear time series analysis for clinical applications. Primarily, self similar characteristics of vocal signals have been obtained by means of a discrete wavelet analysis. Moreover, the approximate entropy of the signals has been calculated as tools for classification. Furthermore, fuzzy c-means clustering has been employed for voice signal classification. Fuzzy membership function has been proposed as a way of quantifying the amount of disorder. The results show that proposed feature vector and classification method are reliable for voice signal analysis and disorder measurement.
authors.affiliation: Department of Mechanical Engineering, Tehran University, Tehran, Iran. b.aghazadeh@me.ut.ac.ir
authors.forename: B S
authors.initials: BS
authors.lastname: Aghazadeh
authors.forename: H
authors.initials: H
authors.lastname: Khadivi
authors.forename: M
authors.initials: M
authors.lastname: Nikkhah-Bahrami
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods*; D013068:Speech Production Measurement / Q000379:methods*; D014832:Voice Disorders / Q000175:diagnosis*