A wavelet-based approach for a continuous analysis of phonovibrograms

• Published in: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2012; pp. 4410 - 4413
• Main Authors: Unger, J., Meyer, T., Doellinger, M., Hecker, D. J., Schick, B., Lohscheller, J.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2012
• Subjects: Accuracy; Colorimetry - methods; Continuous wavelet transforms; Endoscopy - methods; Feature extraction; Humans; Image color analysis; Image Interpretation, Computer-Assisted - methods; Principal component analysis; Reproducibility of Results; Sensitivity and Specificity; Vibration; Vibrations; Video Recording - methods; Vocal Cord Paralysis - diagnosis; Vocal Cord Paralysis - physiopathology; Vocal Cords - pathology; Vocal Cords - physiopathology; Wavelet Analysis
• ISBN: 1424441196; 9781424441198
• ISSN: 1094-687X; 1557-170X
• DOI: 10.1109/EMBC.2012.6346944

Recently, endoscopic high-speed laryngoscopy has been established for commercial use and constitutes a state-of-the-art technique to examine vocal fold dynamics. Despite overcoming many limitations of commonly applied stroboscopy it has not gained widespread clinical application, yet. A major drawback is a missing methodology of extracting valuable features to support visual assessment or computer-aided diagnosis. In this paper a compact and descriptive feature set is presented. The feature extraction routines are based on two-dimensional color graphs called phonovibrograms (PVG). These graphs contain the full spatio-temporal pattern of vocal fold dynamics and are therefore suited to derive features that comprehensively describe the vibration pattern of vocal folds. Within our approach, clinically relevant features such as glottal closure type, symmetry and periodicity are quantified in a set of 10 descriptive features. The suitability for classification tasks is shown using a clinical data set comprising 50 healthy and 50 paralytic subjects. A classification accuracy of 93.2% has been achieved.