Classification of functional voice disorders based on phonovibrograms.
OBJECTIVE
This work presents a computer-aided method for automatically and objectively classifying individuals with healthy and dysfunctional vocal fold vibration patterns as depicted in clinical high-speed (HS) videos of the larynx.


METHODS
By employing a specialized image segmentation and vocal fold movement visualization technique - namely phonovibrography - a novel set of numerical features is derived from laryngeal HS videos capturing the dynamic behavior and the symmetry of oscillating vocal folds. In order to assess the discriminatory power of the features, a support vector machine is applied to the preprocessed data with regard to clinically relevant diagnostic tasks. Finally, the classification performance of the learned nonlinear models is evaluated to allow for conclusions to be drawn about suitability of features and data resulting from different examination paradigms. As a reference, a second feature set is determined which corresponds to more traditional voice analysis approaches.


RESULTS
For the first time an automatic classification of healthy and pathological voices could be obtained by analyzing the vibratory patterns of vocal folds using phonovibrograms (PVGs). An average classification accuracy of approximately 81% was achieved for 2-class discrimination with PVG features. This exceeds the results obtained through traditional voice analysis features. Furthermore, a relevant influence of phonation frequency on classification accuracy was substantiated by the clinical HS data.


CONCLUSIONS
The PVG feature extraction and classification approach can be assessed as being promising with regard to the diagnosis of functional voice disorders. The obtained results indicate that an objective analysis of dysfunctional vocal fold vibration can be achieved with considerably high accuracy. Moreover, the PVG classification method holds a lot of potential when it comes to the clinical assessment of voice pathologies in general, as the diagnostic support can be provided to the voice clinician in a timely and reliable manner. Due to the observed interdependency between phonation frequency and classification accuracy, in future comparative studies of HS recordings of oscillating vocal folds homogeneous frequencies should be taken into account during examination.
Department of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Germany. daniel.voigt@uk-erlangen.de
Daniel
D
Voigt
Michael
M
Döllinger
Thomas
T
Braunschweig
Anxiong
A
Yang
Ulrich
U
Eysholdt
Jörg
J
Lohscheller
D020000:Decision Support Systems, Clinical; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007828:Laryngoscopy; D010363:Pattern Recognition, Automated; D010699:Phonation / Q000502:physiology; D014741:Video Recording; D014827:Vocal Cords / Q000503:physiopathology*; D014832:Voice Disorders / Q000145:classification* / Q000503:physiopathology