title: A generalized procedure for analyzing sustained and dynamic vocal fold vibrations from laryngeal high-speed videos using phonovibrograms.
abstract: OBJECTIVE
  This work presents a computer-based approach to analyze the two-dimensional vocal fold dynamics of endoscopic high-speed videos, and constitutes an extension and generalization of a previously proposed wavelet-based procedure. While most approaches aim for analyzing sustained phonation conditions, the proposed method allows for a clinically adequate analysis of both dynamic as well as sustained phonation paradigms.
  
  
  METHODS
  The analysis procedure is based on a spatio-temporal visualization technique, the phonovibrogram, that facilitates the documentation of the visible laryngeal dynamics. From the phonovibrogram, a low-dimensional set of features is computed using a principle component analysis strategy that quantifies the type of vibration patterns, irregularity, lateral symmetry and synchronicity, as a function of time. Two different test bench data sets are used to validate the approach: (I) 150 healthy and pathologic subjects examined during sustained phonation. (II) 20 healthy and pathologic subjects that were examined twice: during sustained phonation and a glissando from a low to a higher fundamental frequency. In order to assess the discriminative power of the extracted features, a Support Vector Machine is trained to distinguish between physiologic and pathologic vibrations. The results for sustained phonation sequences are compared to the previous approach. Finally, the classification performance of the stationary analyzing procedure is compared to the transient analysis of the glissando maneuver.
  
  
  RESULTS
  For the first test bench the proposed procedure outperformed the previous approach (proposed feature set: accuracy: 91.3%, sensitivity: 80%, specificity: 97%, previous approach: accuracy: 89.3%, sensitivity: 76%, specificity: 96%). Comparing the classification performance of the second test bench further corroborates that analyzing transient paradigms provides clear additional diagnostic value (glissando maneuver: accuracy: 90%, sensitivity: 100%, specificity: 80%, sustained phonation: accuracy: 75%, sensitivity: 80%, specificity: 70%).
  
  
  CONCLUSIONS
  The incorporation of parameters describing the temporal evolvement of vocal fold vibration clearly improves the automatic identification of pathologic vibration patterns. Furthermore, incorporating a dynamic phonation paradigm provides additional valuable information about the underlying laryngeal dynamics that cannot be derived from sustained conditions. The proposed generalized approach provides a better overall classification performance than the previous approach, and hence constitutes a new advantageous tool for an improved clinical diagnosis of voice disorders.
authors.affiliation: Department of Computer Science, Trier University of Applied Sciences, Schneidershof, 54293 Trier, Germany. Electronic address: jakob.unger@lfb.rwth-aachen.de.
authors.forename: Jakob
authors.initials: J
authors.lastname: Unger
authors.affiliation: Department of Otorhinolaryngology and Head and Neck Surgery, University of Munich, Campus Grosshadern, Marchioninistr. 13, 81366 München, Germany.
authors.forename: Maria
authors.initials: M
authors.lastname: Schuster
authors.affiliation: Department of Otorhinolaryngology, Saarland University Hospital, Kirrbergerstr., 66424 Homburg/Saar, Germany.
authors.forename: Dietmar J
authors.initials: DJ
authors.lastname: Hecker
authors.affiliation: Department of Otorhinolaryngology, Saarland University Hospital, Kirrbergerstr., 66424 Homburg/Saar, Germany.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Schick
authors.affiliation: Department of Computer Science, Trier University of Applied Sciences, Schneidershof, 54293 Trier, Germany.
authors.forename: Jörg
authors.initials: J
authors.lastname: Lohscheller
mesh_terms: D000328:Adult; D000368:Aged; D001696:Biomechanical Phenomena; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007828:Laryngoscopy / Q000379:methods*; D007830:Larynx / Q000473:pathology / Q000503:physiopathology*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D010699:Phonation; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D060388:Support Vector Machine; D013997:Time Factors; D014732:Vibration; D014741:Video Recording; D014827:Vocal Cords / Q000473:pathology / Q000503:physiopathology*; D014832:Voice Disorders / Q000175:diagnosis* / Q000473:pathology / Q000503:physiopathology; D014833:Voice Quality; D058067:Wavelet Analysis
keywords: Dynamic phonation; High-speed laryngoscopy; Multiscale product; Voice disorder; Wavelet ridge; Wavelet-based analysis