Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Physical Assessment of the Respiratory Tract IV: Auscultation01:28

Physical Assessment of the Respiratory Tract IV: Auscultation

400
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.
400
Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

Respiratory System Abnormal Finding II: Palpation and Auscultation

453
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:
453
Assessment of Respiration01:23

Assessment of Respiration

1.1K
The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
Subjective Assessment: Nurses interview the patient to gather information directly during the subjective assessment. It includes questions about the individual's medical history, medications, and symptoms, focusing on past respiratory conditions like...
1.1K
Respiratory System Abnormal Finding I: Inspection and Percussion01:30

Respiratory System Abnormal Finding I: Inspection and Percussion

271
Respiratory system abnormalities are a significant concern in healthcare due to their potential to indicate underlying severe conditions like Chronic Obstructive Pulmonary Disease (COPD), asthma, and pneumonia. These abnormalities can often be detected through physical examination methods like inspection and percussion.
Inspection Findings
During an inspection, several findings may suggest the presence of respiratory distress or disease. Pursed-lip breathing, where exhalation is slowed by...
271
Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies01:27

Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies

2.5K
Assessing and diagnosing Chronic Obstructive Pulmonary Disease (COPD) involves a detailed approach that includes a comprehensive review of medical history, physical examination, and a variety of diagnostic tests. This thorough evaluation is essential to ensure an accurate diagnosis and guide effective management strategies.
Medical History
2.5K
Physical Assessment of the Respiratory Tract III: Percussion01:29

Physical Assessment of the Respiratory Tract III: Percussion

392
The respiratory system, fundamental to life, consists of complex structures responsible for gas exchange. The percussion assessment is critical to understanding this system's health and functionality. This non-invasive assessment technique allows healthcare providers to evaluate the density or aeration of the lungs, thereby identifying potential abnormalities.
Percussion in Respiratory Assessment
Percussion evaluates underlying tissue composition with audible and tactile vibrations,...
392

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An Effective Model-Based Voting Classifier for Diabetes Mellitus Classification.

Bioengineering (Basel, Switzerland)·2026
Same author

Non-invasive diagnosis of lung diseases via multimodal feature extraction from breathing audio and chest dynamics.

Computers in biology and medicine·2025
Same author

A review on lung disease recognition by acoustic signal analysis with deep learning networks.

Journal of big data·2023
Same author

Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview.

Diagnostics (Basel, Switzerland)·2023
Same author

Corrigendum to "Compatibility of household appliances with DC microgrid for PV systems" [Heliyon 6 (12), (December 2020) Article e05699].

Heliyon·2021
Same author

Compatibility of household appliances with DC microgrid for PV systems.

Heliyon·2020
Same journal

Novel Parent Survey Measures Sensory Behaviors Incorporating Sensory Modality and Stimulus Intensity.

Heliyon·2026
Same journal

Expression of concern: "SQSTM1/p62 promotes the progression of gastric cancer through epithelial-mesenchymal transition" [Heliyon 10 (2024) e24409].

Heliyon·2026
Same journal

Expression of concern: "TL1A promotes metastasis and EMT process of colorectal cancer" [Heliyon 10 (2024) e24392].

Heliyon·2026
Same journal

Expression of concern: "Factors affecting timing of surgery following neoadjuvant chemoradiation for esophageal cancer" [Heliyon 9 (2023) e23212].

Heliyon·2026
Same journal

Expression of concern: "On stratified single-valued soft topogenous structures" [Heliyon 10 (2024) e27926].

Heliyon·2026
Same journal

Expression of concern: "Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN" [Heliyon 10 (2024) e27198].

Heliyon·2026
See all related articles

Related Experiment Video

Updated: Jul 2, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K

Lung disease recognition methods using audio-based analysis with machine learning.

Ahmad H Sabry1, Omar I Dallal Bashi2, N H Nik Ali3

  • 1Department of Medical Instrumentation Engineering Techniques, Shatt Al-Arab University College, Basra, Iraq.

Heliyon
|February 29, 2024
PubMed
Summary
This summary is machine-generated.

Computer analysis of lung sounds improves respiratory disease diagnosis by reducing subjectivity. Machine learning shows promise for classifying lung conditions, but large-scale studies are needed for clinical adoption.

Keywords:
Audio processingAudio-based analysisClassificationFeature extractionLung disease recognitionLung soundsMachine learningRespiratory sounds

More Related Videos

Author Spotlight: Exploring Breathing Techniques and Digital Solutions for Enhancing Running Performance
06:26

Author Spotlight: Exploring Breathing Techniques and Digital Solutions for Enhancing Running Performance

Published on: September 27, 2024

470
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

Related Experiment Videos

Last Updated: Jul 2, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
Author Spotlight: Exploring Breathing Techniques and Digital Solutions for Enhancing Running Performance
06:26

Author Spotlight: Exploring Breathing Techniques and Digital Solutions for Enhancing Running Performance

Published on: September 27, 2024

470
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

Area of Science:

  • Medical Diagnostics
  • Computational Biology
  • Signal Processing

Background:

  • Automated computer-based approaches and advanced recording techniques enhance lung sound diagnostics, minimizing subjectivity.
  • Computer-based lung sound analysis enables thorough evaluation of lung sound features, including behavior analysis, measurement, noise suppression, and graphical representation.

Purpose of the Study:

  • To provide an overview of computer-based lung sound analysis for respiratory disease diagnosis.
  • To discuss the methodology, datasets, feature extraction, pre-processing, artifact removal, sound separation, and machine learning algorithms used in lung sound analysis.
  • To identify literature gaps and highlight the potential of machine learning in classifying respiratory diseases.

Main Methods:

  • Literature review and survey of existing studies on sound-based lung disease classification.
  • Discussion of machine learning algorithms, including deep learning and wavelet transform, applied to lung audio signals.
  • Analysis of datasets, feature extraction, pre-processing, and artifact removal techniques.

Main Results:

  • Machine learning algorithms applied to lung sound analysis demonstrate promising results for respiratory disease classification.
  • The study identifies key elements and methodologies in sound-based lung disease diagnosis.
  • Literature gaps in large-scale investigations for clinical adoption are highlighted.

Conclusions:

  • Sound-based machine learning offers a promising avenue for the classification of respiratory diseases.
  • Further large-scale investigations are crucial to validate findings and promote widespread clinical adoption.
  • The findings are valuable for physicians and researchers in the field of sound-signal-based machine learning.