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Related Concept Videos

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.
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Physical Assessment of the Respiratory Tract IV: Auscultation01:28

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Suctioning the Nasopharyngeal Airway01:29

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Suctioning the Oropharyngeal Airway01:25

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In preparing for oropharyngeal airway suctioning, a nurse must gather all necessary equipment, including a suction unit with tubing, a prepackaged suction kit, sterile gloves, water or saline for irrigation, a water-soluble lubricant, and additional personal protective equipment (such as a gown, mask, and goggles) to control infections.
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Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

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Image Acquisition using Portable Sonography for Emergency Airway Management
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Smart Phone based Snoring Sound analysis to Identify Upper Airway Obstructions.

Mrunal N Markandeya, Udantha R Abeyratne

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Smartphones can detect upper airway obstructions by analyzing snoring sounds. This technology uses high-frequency snoring audio to identify potential cases of obstructive sleep apnea (OSA).

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    Area of Science:

    • Biomedical Engineering
    • Sleep Medicine
    • Acoustic Signal Processing

    Background:

    • Obstructive sleep apnea (OSA) involves upper airway obstructions during sleep, often associated with snoring.
    • High-frequency shifts in snoring sounds correlate with airway narrowing during apnea/hypopnea events.
    • Existing wideband microphone systems for detecting these shifts are costly and impractical for widespread use.

    Purpose of the Study:

    • To investigate the feasibility of using smartphone microphones to analyze snoring sounds for identifying upper airway obstructions.
    • To develop and validate models for classifying sleep sound epochs as non-apnea or apnea using smartphone technology.

    Main Methods:

    • Collected overnight respiratory sound data from 8 patients using a Samsung Galaxy S3 smartphone during Polysomnography (PSG).
    • Developed logistic regression models using snoring sound features from 700 30-second epochs.
    • Analyzed sound data within the 20Hz-15kHz and 20Hz-22kHz frequency bands to classify events as apnea or non-apnea.

    Main Results:

    • Models using the 20Hz-22kHz band achieved higher performance: 94-97% accuracy, 93-100% sensitivity, and a kappa index of 0.86-0.91.
    • Compared to models using the 20Hz-15kHz band (89-93% accuracy, 70-78% sensitivity), the broader band showed significant improvement.
    • High-frequency snoring sounds (15-22kHz) captured by smartphones contain valuable information for detecting upper airway obstructions.

    Conclusions:

    • Smartphone-based analysis of high-frequency snoring sounds is a feasible method for identifying upper airway obstructions.
    • This non-contact technology offers a potential low-cost solution for screening obstructive sleep apnea (OSA).
    • Further development could lead to accessible home-use or population screening tools for OSA detection.