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Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice
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A wavelet-based approach for a continuous analysis of phonovibrograms.

Jakob Unger1, Tobias Meyer, Michael Doellinger

  • 1Department of Computer Science, University of Applied Science Trier, Trier, Germany.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary

High-speed laryngoscopy, a new vocal fold examination technique, now has a method to extract key features. This phonovibrogram (PVG) approach enables accurate classification of vocal fold dynamics.

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Area of Science:

  • Otolaryngology
  • Biomedical Engineering
  • Medical Imaging

Background:

  • Endoscopic high-speed laryngoscopy offers advanced vocal fold dynamics visualization but lacks standardized feature extraction for clinical use.
  • Current methods like stroboscopy have limitations, hindering the widespread adoption of high-speed laryngoscopy.
  • Objective assessment and computer-aided diagnosis are needed to leverage high-speed laryngoscopy's full potential.

Purpose of the Study:

  • To present a novel feature set for analyzing vocal fold dynamics from high-speed laryngoscopy.
  • To develop a methodology for extracting clinically relevant features using phonovibrograms (PVG).
  • To evaluate the classification performance of the proposed features for distinguishing healthy from paralytic vocal folds.

Main Methods:

  • Feature extraction based on two-dimensional color graphs called phonovibrograms (PVG).
  • Quantification of 10 clinically relevant features, including glottal closure type, symmetry, and periodicity.
  • Classification using a dataset of 50 healthy and 50 paralytic subjects.

Main Results:

  • The phonovibrograms (PVG) successfully capture spatio-temporal vocal fold dynamics.
  • A compact set of 10 features comprehensively describes vocal fold vibration patterns.
  • Achieved a classification accuracy of 93.2% for distinguishing healthy and paralytic subjects.

Conclusions:

  • The presented feature set and PVG-based methodology effectively support visual assessment and computer-aided diagnosis in high-speed laryngoscopy.
  • This approach facilitates objective quantification of vocal fold dynamics, paving the way for wider clinical application.
  • High-speed laryngoscopy with PVG feature extraction shows significant potential for diagnosing vocal fold pathologies.