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Related Concept Videos

Physical Assessment of the Respiratory Tract IV: Auscultation01:28

Physical Assessment of the Respiratory Tract IV: Auscultation

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

Respiratory System Abnormal Finding II: Palpation and Auscultation

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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:
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Classification of Signals01:30

Classification of Signals

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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...
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Heart Sounds01:15

Heart Sounds

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Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
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Assessment of Respiration01:23

Assessment of Respiration

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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...
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Larynx01:21

Larynx

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The human larynx, often referred to as the voice box, is an intricate organ located in the neck. It serves as a pathway for air to enter the lungs during respiration and is an essential component of voice production.
Anatomy of the Larynx
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Related Experiment Video

Updated: Mar 19, 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

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Lung sound classification using cepstral-based statistical features.

Nandini Sengupta1, Md Sahidullah2, Goutam Saha1

  • 1Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, Kharagpur 721302, India.

Computers in Biology and Medicine
|June 11, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces novel statistical features from mel-frequency cepstral coefficients (MFCCs) for lung sound classification. These new features improve disease identification accuracy, even in noisy conditions.

Keywords:
Artificial neural network (ANN)AuscultationDiscrete wavelet transform (DWT)Mel-frequency cepstral coefficients (MFCCs)Spectral featuresStatistical features

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Area of Science:

  • Medical signal processing
  • Respiratory diagnostics

Background:

  • Lung sounds offer vital insights into pulmonary conditions.
  • Current methods for lung sound analysis face challenges with noise and efficiency.

Purpose of the Study:

  • To develop and evaluate novel spectral features for accurate lung sound classification.
  • To enhance the robustness of lung sound analysis in the presence of noise.

Main Methods:

  • Extraction and statistical analysis of mel-frequency cepstral coefficients (MFCCs) from lung sound signals.
  • Utilizing artificial neural networks (ANN) for classification of normal, wheeze, and crackle lung sounds.
  • Experimental optimization of feature extraction parameters and evaluation under varying signal-to-noise ratios (SNRs).

Main Results:

  • Proposed statistical features derived from MFCCs significantly outperformed wavelet-based and standard cepstral features.
  • The novel features demonstrated superior recognition accuracy for lung sound classification.
  • Enhanced robustness was observed in noisy environments, with better performance at low SNRs.

Conclusions:

  • Statistical features from MFCCs offer a promising approach for accurate and robust lung sound analysis.
  • The developed method has the potential to improve computer-aided diagnosis of respiratory diseases.