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Related Concept Videos

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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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 I: Inspection and Percussion01:30

Respiratory System Abnormal Finding I: Inspection and Percussion

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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...
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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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Alterations in Respiration II01:30

Alterations in Respiration II

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There are numerous types of normal and abnormal respiration. Based on ventilatory movements, breathing patterns are classified as regular, deep, or shallow. Examples include Biot's breathing, Cheyne-Stokes respiration, Kussmaul's breathing, hyperventilation, and hypoventilation. Each pattern is clinically significant and aids in evaluating patients.
In Biot's breathing, the respiratory rate and depth are irregular, alternating between periods of deep gasping and apnea. Common causes...
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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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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition.

Rizwana Zulfiqar1, Fiaz Majeed1, Rizwana Irfan2

  • 1Faculty of Computing and Information Technology, University of Gujrat, Gujrat, Pakistan.

Frontiers in Medicine
|December 6, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using Artificial Noise Addition (ANA) and deep convolutional neural networks (CNNs) to accurately classify seven abnormal respiratory sounds. The approach enhances diagnostic capabilities for lung diseases, aiming to reduce mortality rates.

Keywords:
abnormal respiratory soundscontinuous adventitious sounds (CAS)deep CNNdiscontinuous adventitious sounds (DAS)respiratory sounds

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Area of Science:

  • Medical Diagnostics
  • Pulmonology
  • Signal Processing

Background:

  • Respiratory sound (RS) analysis is crucial for diagnosing lung pathologies.
  • Traditional methods rely on subjective auscultation, limiting accuracy.
  • Lung disease is a leading cause of global mortality, necessitating improved diagnostic tools.

Purpose of the Study:

  • To develop and evaluate a novel framework for accurate classification of abnormal respiratory sounds.
  • To improve the detection and differentiation of seven types of abnormal respiratory sounds (continuous and discontinuous).
  • To enhance the accuracy of respiratory sound analysis for better patient outcomes.

Main Methods:

  • Application of Fourier analysis for visual inspection of abnormal respiratory sounds.
  • Spectrum analysis utilizing Artificial Noise Addition (ANA) combined with deep convolutional neural networks (CNNs).
  • Development of an adaptive mechanism to add noise, enhancing sound feature detectability.

Main Results:

  • The proposed framework demonstrated superior performance compared to previous techniques.
  • Artificial Noise Addition (ANA) significantly improved the accuracy of identifying abnormal respiratory sound features.
  • Simultaneous classification of seven distinct abnormal respiratory sound classes was achieved with high accuracy.

Conclusions:

  • The integration of ANA and CNNs provides a robust method for classifying abnormal respiratory sounds.
  • This approach offers a significant advancement in the objective analysis of lung sounds.
  • The findings support the potential of this framework for early and accurate diagnosis of respiratory diseases.