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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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Respiratory System Abnormal Finding I: Inspection and Percussion01:30

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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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Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

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Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
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Respiratory Assessment: Purpose and Indications01:19

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Respiratory assessment is a cornerstone of nursing assessments, crucial for the early detection of patient deterioration. This evaluation transcends routine procedures, representing a critical skill nurses must master to ensure optimal patient care.
Objectives and Importance:
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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D-Cov19Net: A DNN based COVID-19 detection system using lung sound.

Sukanya Chatterjee1, Jishnu Roychowdhury1, Anilesh Dey1

  • 1Department of Electronics and Communication Engineering, Narula Institute of Technology, Agarpara, India.

Journal of Computational Science
|December 20, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning model, D-Cov19Net, accurately detects COVID-19 using only respiratory sounds. This simple, fast, and accurate system aids in early diagnosis, even with limited resources.

Keywords:
Auto-diagnosis systemCOVID-19 DetectionConvolution Neural Network (CNN)Deep LearningLung/Respiratory sound

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Respiratory Medicine

Background:

  • The COVID-19 pandemic highlighted the need for rapid and accessible diagnostic tools.
  • Limitations in traditional testing methods necessitate innovative approaches for widespread screening.
  • The potential of physiological signals, like respiratory sounds, for disease detection is increasingly recognized.

Purpose of the Study:

  • To develop an automated system for COVID-19 detection using a single input parameter.
  • To leverage deep learning for accurate and efficient diagnosis of respiratory infections.
  • To create a user-friendly and feasible tool for early COVID-19 identification.

Main Methods:

  • A Deep Convolution Neural Network (D-Cov19Net) was designed for audio signal analysis.
  • The model was trained on a dataset of 23,592 respiratory sound recordings.
  • Performance was evaluated using standard metrics like Area Under the Curve (AUC) and sensitivity.

Main Results:

  • D-Cov19Net achieved a high Area Under the Curve (AUC) of 0.972.
  • The model demonstrated excellent sensitivity, reaching 0.983 after 100 training epochs.
  • The system proved effective in distinguishing COVID-19 positive cases from others based on lung sounds.

Conclusions:

  • D-Cov19Net offers a highly accurate and sensitive method for COVID-19 auto-diagnosis.
  • The system's simplicity, feasibility, and speed make it valuable for biomedical technology applications.
  • This approach supports remote patient monitoring and social distancing during public health crises.