Quantifying Spatiotemporal Properties of Vocal Fold Dynamics Based on a Multiscale Analysis of Phonovibrograms

• Published in: IEEE transactions on biomedical engineering Vol. 61; no. 9; pp. 2422 - 2433
• Main Authors: Unger, Jakob, Hecker, Dietmar J., Kunduk, Melda, Schuster, Maria, Schick, Bernhard, Lohscheller, Joerg
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Classification; Correlation; Dynamic tests; Dynamics; Eigenvalues and eigenfunctions; Feature extraction; Female; High speed; Humans; Irregularities; Male; Middle Aged; Sound Spectrography - methods; Speech disorders; Vibration; Vibrations; Video Recording - methods; Videos; Vocal Cords - physiology; Voice; Wavelet Analysis; Wavelet transforms; multiscale product (MSP); high speed videoendoscopy (HSV); wavelet analysis; vocal fold dynamics; Computer-aided analysis
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2014.2318774

In order to objectively assess the laryngeal vibratory behavior, endoscopic high-speed cameras capture several thousand frames per second of the vocal folds during phonation. However, judging all inherent clinically relevant features is a challenging task and requires well-founded expert knowledge. In this study, an automated wavelet-based analysis of laryngeal high-speed videos based on phonovibrograms is presented. The phonovibrogram is an image representation of the spatiotemporal pattern of vocal fold vibration and constitutes the basis for a computer-based analysis of laryngeal dynamics. The features extracted from the wavelet transform are shown to be closely related to a basic set of video-based measurements categorized by the European Laryngological Society for a subjective assessment of pathologic voices. The wavelet-based analysis further offers information about irregularity and lateral asymmetry and asynchrony. It is demonstrated in healthy and pathologic subjects as well as for a surgical group that was examined before and after the removal of a vocal fold polyp. The features were found to not only classify glottal closure characteristics but also quantify the impact of pathologies on the vibratory behavior. The interpretability and the discriminative power of the proposed feature set show promising relevance for a computer-assisted diagnosis and classification of voice disorders.