Integrated analyzer and classifier of glottographic signals

Area of Science:

• Laryngology
• Speech Science
• Biomedical Engineering

Background:

• Electroglottography (EGG) and photoglottography (PGG) are non-invasive methods for assessing laryngeal vibration patterns.
• Quantitative measures like open quotient and speed quotient from glottographic signals may indicate pathological phonation.

Purpose of the Study:

• To develop and evaluate an integrated system for automatic analysis and classification of digitized EGG and PGG signals.
• To examine the system's utility in identifying vocal fold abnormalities and aiding in the diagnosis of voice disorders.

Main Methods:

• Implementation of an integrated analyzer and classifier for glottographic signals.
• Development of feature extraction techniques and a statistical classification method.
• Validation using training and test datasets from normal subjects and patients with laryngeal paralysis.

Main Results:

• The developed system automatically calculates key measures from EGG and PGG signals.
• The system demonstrated reliability in analyzing and classifying glottographic signals from both normal and pathological cases.
• The classification method showed potential for aiding in the diagnosis of voice disorders.

Conclusions:

• The integrated glottographic analysis system is a valuable tool for quantitative phonatory pathophysiology studies.
• The system can assist clinicians in the quantitative examination of glottographic signals for voice disorder assessment.
• Further evaluation is needed for direct clinical application in documenting phonatory function in patients with voice disorders.