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Related Concept Videos

Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

Respiratory System Abnormal Finding II: Palpation and Auscultation

In assessing respiratory abnormalities, palpation and auscultation are critical tools for detecting and interpreting various pathophysiological changes. These techniques provide insight into underlying disorders by evaluating tactile sensations and sounds produced by the respiratory system.
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:
Assessment of Airway, Skin Color, and Use of Accessory Muscles01:30

Assessment of Airway, Skin Color, and Use of Accessory Muscles

A thorough assessment of respiratory health is paramount in clinical settings to identify and manage respiratory distress and ensure adequate oxygenation. This article elaborates on the critical aspects of respiratory evaluation, including airway assessment, skin color examination, and the observation of accessory muscle use, which are integral to effectively diagnosing and managing patients with respiratory conditions.
Introduction
The initial evaluation of a patient's respiratory system...
Physical Assessment of the Respiratory Tract II: Palpation01:24

Physical Assessment of the Respiratory Tract II: Palpation

Physical assessment of the respiratory tract is critical in identifying potential health issues. One key component of this assessment is palpation, a technique healthcare providers use to assess the body for abnormalities. This content explores the method of palpation in evaluating the respiratory tract, focusing on thoracic palpation and tactile fremitus.
Thoracic Palpation
Thoracic palpation detects tenderness, masses, lesions, respiratory excursions, and vocal fremitus. The nurse assesses...

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Related Experiment Video

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Published on: July 22, 2025

Vocal folds disorder detection using pattern recognition methods.

Jianglin Wang1, Cheolwoo Jo

  • 1SASPL, Changwon National University, Changwon, Korea 641-773.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
Summary

This study compares Hidden Markov Models, Gaussian Mixture Models, and Support Vector Machines for pathological voice classification. The Gaussian Mixture Model achieved superior classification rates for detecting vocal disorders.

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Area of Science:

  • Biomedical Engineering
  • Speech Technology
  • Pattern Recognition

Background:

  • Pathological voice diagnosis is crucial in biomedical speech technology.
  • Accurate classification of vocal disorders is an ongoing challenge.
  • Previous studies utilized Artificial Neural Networks (ANN) for voice analysis.

Purpose of the Study:

  • To classify pathological voices using Hidden Markov Models (HMM), Gaussian Mixture Models (GMM), and Support Vector Machines (SVM).
  • To compare the performance of HMM, GMM, and SVM against previous ANN results.
  • To identify the most effective pattern recognition method for pathological voice detection.

Main Methods:

  • Collected speech data from normal and pathological subjects.
  • Extracted six voice parameters: Jitter, Shimmer, NHR, SPI, APQ, and RAP.
  • Applied HMM, GMM, and SVM for pattern recognition to classify speech samples.

Main Results:

  • The Gaussian Mixture Model (GMM) demonstrated superior classification rates compared to HMM and SVM.
  • GMM effectively distinguished between normal and pathological speech patterns.
  • Performance was benchmarked against established Artificial Neural Network (ANN) methods.

Conclusions:

  • GMM-based methods offer a highly effective approach for pathological voice diagnosis.
  • The study highlights the potential of GMM in advancing speech technology for biomedical applications.
  • Further research can explore hybrid models for enhanced pathological voice detection.