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Related Concept Videos

Larynx01:21

Larynx

The human larynx, often referred to as the voice box, is an intricate organ located in the neck. It serves as a pathway for air to enter the lungs during respiration and is an essential component of voice production.
Anatomy of the Larynx
The larynx consists of various components, including cartilage, muscles, and vocal cords. Its structure includes three large unpaired cartilages—the thyroid, cricoid, and epiglottis—and three smaller paired cartilages—the arytenoids, corniculates, and...
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Physical Assessment of the Respiratory Tract IV: Auscultation01:28

Physical Assessment of the Respiratory Tract IV: Auscultation

Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
Breath Sounds
Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.

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Related Experiment Video

Updated: Jun 16, 2026

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis (ALS)
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A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis (ALS)

Published on: February 21, 2011

Classification of functional voice disorders based on phonovibrograms.

Daniel Voigt1, Michael Döllinger, Thomas Braunschweig

  • 1Department of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Germany. daniel.voigt@uk-erlangen.de

Artificial Intelligence in Medicine
|February 9, 2010
PubMed
Summary

This study introduces a computer-aided method using phonovibrography (PVG) to automatically classify vocal fold vibration patterns from high-speed videos. The novel PVG features achieved 81% accuracy, outperforming traditional methods for diagnosing voice disorders.

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A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis (ALS)
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A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis (ALS)

Published on: February 21, 2011

Area of Science:

  • Laryngology and Voice Science
  • Medical Imaging and Signal Processing
  • Computational Pathology

Background:

  • Clinical diagnosis of vocal fold dysfunction often relies on subjective interpretation of laryngeal imaging.
  • High-speed (HS) videoendoscopy provides dynamic visualization of vocal fold vibration but lacks automated objective analysis tools.
  • Developing objective methods is crucial for accurate and timely diagnosis of voice disorders.

Purpose of the Study:

  • To present a novel computer-aided method for objective classification of healthy versus dysfunctional vocal fold vibration patterns.
  • To derive and evaluate new numerical features from laryngeal HS videos using phonovibrography (PVG).
  • To assess the diagnostic performance of the proposed method compared to traditional voice analysis techniques.

Main Methods:

  • Utilized specialized image segmentation and vocal fold movement visualization (phonovibrography) on laryngeal HS videos.
  • Extracted novel numerical features capturing dynamic behavior and symmetry of oscillating vocal folds.
  • Employed support vector machines for classification and evaluated performance on clinical data, comparing PVG features with traditional voice analysis features.

Main Results:

  • Achieved an average classification accuracy of approximately 81% for discriminating between healthy and pathological voices using PVG features.
  • Demonstrated that PVG-based classification significantly outperformed traditional voice analysis features.
  • Identified a significant influence of phonation frequency on classification accuracy, substantiated by clinical HS data.

Conclusions:

  • The PVG feature extraction and classification approach shows significant promise for the objective diagnosis of functional voice disorders.
  • The method enables highly accurate, objective analysis of dysfunctional vocal fold vibration, offering potential for general voice pathology assessment.
  • Future studies should consider homogeneous phonation frequencies to refine comparative analyses of HS recordings.