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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:
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.
Assessment of Respiration01:23

Assessment of Respiration

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 asthma or COPD,...
Respiratory System Abnormal Finding I: Inspection and Percussion01:30

Respiratory System Abnormal Finding I: Inspection and Percussion

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...
Physical Assessment of the Respiratory Tract III: Percussion01:29

Physical Assessment of the Respiratory Tract III: Percussion

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,...
Assessment of the Cardiovascular System IV: Auscultation01:25

Assessment of the Cardiovascular System IV: Auscultation

Cardiac auscultation is a clinical skill used to assess heart function and detect abnormalities. It involves listening to heart sounds at specific anatomical locations through a stethoscope.
Normal Heart Sounds
S1 (First Heart Sound)-
S1 is made by the closure of the mitral and tricuspid valves (atrioventricular valves), marking the beginning of systole.
S2 (Second Heart Sound)-
S2 is made by the closure of the aortic and pulmonic valves (semilunar valves), marking the end of the systole.

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Related Experiment Video

Updated: May 14, 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

A multiresolution analysis for detection of abnormal lung sounds.

Dimitra Emmanouilidou1, Kailash Patil, James West

  • 1Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces novel signal processing tools for analyzing noisy pediatric lung sounds. The biomimetic model effectively distinguishes abnormal lung sounds like crackles and wheezes from normal breathing.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Pulmonology

Background:

  • Automated lung sound analysis aids pulmonary disease diagnosis, especially in resource-limited areas.
  • Accurate detection of abnormal lung sounds (crackles, wheezes) is crucial for timely intervention.
  • Existing methods struggle with non-ideal, noisy auscultation recordings.

Purpose of the Study:

  • To develop advanced signal processing tools for analyzing pediatric lung auscultations.
  • To enhance the accuracy of abnormal lung sound detection in noisy environments.
  • To create a robust model for differentiating pathological lung sounds from normal breathing.

Main Methods:

  • Developed a biomimetic multi-resolution analysis model for lung sound signals.
  • Focused on analyzing spectro-temporal modulation details within lung sounds.
  • Investigated joint spectral and temporal variations for improved signal characterization.

Main Results:

  • The proposed methodology provides detailed spectro-temporal analysis of lung sounds.
  • The model demonstrated superior robustness compared to traditional frequency-based techniques.
  • Successfully distinguished between crackles, wheezes, and normal breathing sounds under noisy conditions.

Conclusions:

  • The developed signal processing tools offer a robust approach for analyzing pediatric lung sounds.
  • This technique holds significant potential for improving diagnostic accessibility in low-resource settings.
  • The biomimetic model advances the automated detection of pulmonary abnormalities via auscultation.