title: Analysis of vocal disorders in a feature space.
abstract: This paper provides a way to classify vocal disorders for clinical applications. This goal is achieved by means of geometric signal separation in a feature space. Typical quantities from chaos theory (like entropy, correlation dimension and first lyapunov exponent) and some conventional ones (like autocorrelation and spectral factor) are analysed and evaluated, in order to provide entries for the feature vectors. A way of quantifying the amount of disorder is proposed by means of a healthy index that measures the distance of a voice sample from the centre of mass of both healthy and sick clusters in the feature space. A successful application of the geometrical signal separation is reported, concerning distinction between normal and disordered phonation.
authors.affiliation: Max-Planck-Institut für Physik komplexer Systeme, Nöthnitzer Str. 38, D 01187, Dresden, Germany. lorenzo@mpipks-dresden.mpg.de
authors.forename: L
authors.initials: L
authors.lastname: Matassini
authors.forename: R
authors.initials: R
authors.lastname: Hegger
authors.forename: H
authors.initials: H
authors.lastname: Kantz
authors.forename: C
authors.initials: C
authors.lastname: Manfredi
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D012720:Severity of Illness Index; D012815:Signal Processing, Computer-Assisted; D013064:Speech Disorders / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology; D014827:Vocal Cords / Q000503:physiopathology / Q000601:surgery; D014832:Voice Disorders / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology