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Related Concept Videos

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.
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 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,...
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...
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...
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...

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Related Experiment Video

Updated: Jul 17, 2026

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

Published on: September 19, 2025

Computerized classification of normal and abnormal lung sounds by multivariate linear autoregressive model.

H G Martinez-Hernandez1, C T Aljama-Corrales, R Gonzalez-Camarena

  • 1UAM Iztapalapa/CBI, Mexico City, Mexico.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 7, 2007
PubMed
Summary

This study used a microphone array and advanced signal processing to classify lung sounds, achieving 87.68% accuracy in identifying pulmonary conditions. The findings highlight the potential of acoustic analysis for diagnosing diffuse interstitial pneumonia.

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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Area of Science:

  • Medical Technology
  • Signal Processing
  • Pulmonology

Background:

  • Accurate classification of pulmonary conditions is crucial for effective treatment.
  • Traditional auscultation methods may miss subtle acoustic changes in lung sounds.
  • Advanced signal processing offers potential for objective lung sound analysis.

Purpose of the Study:

  • To develop and validate a system for multichannel lung sound acquisition and classification.
  • To evaluate the effectiveness of a multivariate autoregressive (MAR) model combined with dimensionality reduction techniques for feature extraction.
  • To assess the performance of a supervised neural network in classifying normal and abnormal pulmonary acoustic information.

Main Methods:

  • Utilized a 25-sensor microphone array for multichannel lung sound acquisition.
  • Applied a multivariate autoregressive (MAR) model for feature extraction from lung sound signals.
  • Employed Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) for dimensionality reduction of feature vectors.
  • Classified acoustic features using a supervised neural network trained with the backpropagation algorithm (Levenberg-Marquardt rule).

Main Results:

  • The combination of MAR modeling and PCA achieved the highest average correct classification rate of 87.68% on unseen acoustic data.
  • The system demonstrated effectiveness in classifying normal and abnormal pulmonary acoustic information, particularly in cases of diffuse interstitial pneumonia.
  • Performance metrics included correct classification percentage during training, testing, and validation, alongside sensitivity, specificity, and overall performance.

Conclusions:

  • A microphone array-based system integrated with MAR and PCA offers significant advantages for classifying pulmonary acoustic information.
  • The study suggests that not only crackles but also the basal respiratory signal can indicate the severity of diffuse interstitial pneumonia.
  • This approach holds promise for improved diagnosis and severity assessment of lung diseases.