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Related Concept Videos

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...
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...
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.
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Ventilatory Modes01:14

Ventilatory Modes

Mechanical ventilators are life-saving devices that support or replace spontaneous breathing. They deliver breaths to patients through varying methods known as ventilator modes. Understanding these modes is critical for healthcare providers managing patients with respiratory failure.
There are three ventilatory modes: full support, partial support, and spontaneous. These are described below.
Full Support Modes
Full support modes include controlled mechanical ventilation, continuous mandatory...

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Related Experiment Video

Updated: May 25, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Variational mode decomposition based prediction model for cough sounds using Xception-GRU classifier.

S Jayalakshmy1, B Lakshmipriya2

  • 1Department of Electronics and Communication Engineering, IFET College of Engineering, Villupuram, India.

Computers in Biology and Medicine
|May 23, 2026
PubMed
Summary

This study developed an AI model to accurately detect abnormal coughs, a key indicator for respiratory diseases. The system uses advanced signal processing and machine learning for improved diagnostic accuracy.

Keywords:
CoughGated recurrent unitSpectrogramVariational mode decompositionXception

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

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Last Updated: May 25, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Area of Science:

  • Medical Diagnostics
  • Signal Processing
  • Artificial Intelligence

Background:

  • Cough analysis is crucial for diagnosing respiratory illnesses.
  • Human auditory perception struggles to detect subtle pathological changes in cough sounds.
  • Computer-aided diagnosis is increasingly important for accurate and efficient medical assessments.

Purpose of the Study:

  • To develop a novel prediction model for respiratory disease diagnosis using cough sound analysis.
  • To investigate the effectiveness of Variational Mode Decomposition (VMD) and Xception models in feature extraction.
  • To enhance cough sound classification accuracy through advanced signal processing and machine learning techniques.

Main Methods:

  • Cough sounds were decomposed into five intrinsic mode functions (IMFs) using Variational Mode Decomposition (VMD).
  • Spectrograms of each IMF were visualized on the bark frequency scale.
  • Features were extracted using the Xception model and classified with a Gated Recurrent Unit (GRU) classifier.

Main Results:

  • The developed model achieved a classification accuracy of 97.22%.
  • The fusion strategy effectively reduced feature dimensionality, improving performance.
  • Optimal frequency scale representation for cough sounds was identified, enhancing discrimination.

Conclusions:

  • The combination of VMD, bark scale spectrograms, Xception, and GRU significantly improves cough sound classification.
  • This approach offers a promising computer-aided tool for early respiratory disease detection.
  • The study highlights the potential of advanced signal processing and AI in medical diagnostics.