Nonlinear Analysis and Classification of Vocal Disorders

  title={Nonlinear Analysis and Classification of Vocal Disorders},
  author={B Aghazadeh and Heydar Khadivi and Mansour Nikkhah-Bahrami},
  journal={2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
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… CONTINUE READING

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