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Related Concept Videos

Trachea01:22

Trachea

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The trachea, commonly known as the windpipe, is a vital part of the human respiratory system. It serves as a passageway for air to travel between the larynx and the bronchi, allowing oxygen to reach the lungs. Let's explore its anatomical features, dimensions, layers of the tracheal wall, associated muscles, and the functions of its parts.
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Sleep Apnea01:21

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Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...
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Tracheostomy Decannulation01:21

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Tracheostomy decannulation is a significant milestone in the liberation of mechanically ventilated patients. Despite its importance, there is no universally accepted protocol for this procedure. This demands an evidence-based, individualized approach.
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Physical Assessment of the Respiratory Tract IV: Auscultation01:28

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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.
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Tracheostomy Suctioning II: Procedure01:23

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Tracheostomy suctioning is a vital nursing procedure that involves removing secretions from the tracheostomy tube to maintain airway patency and prevent respiratory complications. Nurses need to understand the proper technique for tracheostomy suctioning to ensure patient safety and comfort. In this guide, we will outline the step-by-step process for performing tracheostomy suctioning, including preparing the sterile field, donning personal protective equipment (PPE), lubricating and connecting...
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Oxygen Delivering System II: Venturi Mask and Transtracheal Oxygen01:16

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Oxygen therapy is a pivotal aspect of medical care, particularly for patients with respiratory ailments. Two prominent oxygen-delivering systems include the Venturi mask and the transtracheal oxygen catheter.
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Related Experiment Video

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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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Removing of Snoring Segments from Tracheal Breathing Sounds using a Wavelet-based Algorithm.

Nasim Montazeri Ghahjaverestan, Shumit Saha, Bojan Gavrilovic

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
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    Summary

    This study presents an automatic algorithm to remove snoring sounds from tracheal breathing sounds during sleep. The method effectively separates snoring, improving analysis of respiratory airflow and upper airway function.

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    Area of Science:

    • Biomedical Engineering
    • Respiratory Physiology
    • Signal Processing

    Background:

    • Tracheal sounds offer insights into respiratory airflow but are often obscured by snoring during sleep.
    • Snoring's broad spectral content overlaps with breathing sounds, necessitating snoring removal for accurate respiratory assessment.

    Purpose of the Study:

    • To develop an automatic, unsupervised wavelet-based algorithm for snoring removal from tracheal sound recordings.
    • To enable separate analysis of tracheal breathing sounds and snoring patterns for improved clinical insights.

    Main Methods:

    • Recorded full-night polysomnography and tracheal sounds from 9 subjects with varying airway obstruction.
    • Manually identified snoring-contaminated segments, categorized them into discrete or continuous patterns.
    • Applied iterative wave-based filtering, optimized for spectral separation of snoring and breathing components.

    Main Results:

    • The algorithm successfully separated snoring from tracheal breathing sounds across different snoring patterns.
    • Visual inspection and spectral density comparisons confirmed effective denoising.
    • The overall snoring detectability rate after removal was less than 2%.

    Conclusions:

    • The developed algorithm effectively removes snoring artifacts from tracheal sound recordings.
    • This facilitates independent analysis of breathing sounds and snoring, aiding in respiratory airflow assessment and understanding upper airway pathophysiology during sleep.