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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

231
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...
231
Cardiopulmonary Resuscitation II: ACLS Airway Management01:22

Cardiopulmonary Resuscitation II: ACLS Airway Management

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Airway management is a key skill in emergency and critical care settings, as maintaining a clear airway is essential for adequate oxygenation and ventilation.Head Tilt-Chin Lift TechniqueThe head tilt-chin lift maneuver is an essential technique primarily used in patients without suspected cervical spine injuries. To perform this maneuver, one hand is placed on the patient’s forehead, and gentle pressure is applied backward to tilt the head. The fingertips of the other hand are positioned...
148

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Related Experiment Video

Updated: Sep 27, 2025

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
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Obstructive Sleep Apnea Detection Scheme Based on Manually Generated Features and Parallel Heterogeneous Deep

Shiliang Shao, Guangjie Han, Ting Wang

    IEEE Journal of Biomedical and Health Informatics
    |April 13, 2022
    PubMed
    Summary

    A novel system uses deep learning and heart rate variability (HRV) from ECG signals for accurate obstructive sleep apnea (OSA) detection via the Internet of Medical Things (IoMT). This method improves OSA diagnosis accuracy, offering a new direction for remote monitoring.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence in Medicine
    • Cardiology

    Background:

    • Obstructive sleep apnea (OSA) is a prevalent sleep disorder linked to severe cardiovascular and cerebrovascular diseases.
    • Remote diagnosis of OSA is facilitated by the Internet of Medical Things (IoMT), enabling cloud-based analysis of physiological signals.
    • Existing OSA detection methods require improvement in accuracy for effective remote patient management.

    Purpose of the Study:

    • To propose and investigate a novel obstructive sleep apnea (OSA) detection system.
    • To enhance OSA detection accuracy using a parallel heterogeneous deep learning model within an IoMT framework.
    • To explore the efficacy of using heart rate variability (HRV) features for OSA recognition.

    Main Methods:

    • A novel OSA detection system utilizing manually generated features and a parallel heterogeneous deep learning model.
    • Extraction of short-term heart rate variability (HRV) signals from ECG signals.
    • Combination of 1-D HRV sequences and 2-D HRV time-frequency spectrum images as inputs for the deep learning network.
    • Validation using the Physionet Apnea-ECG public database.

    Main Results:

    • The proposed deep learning model achieved high accuracy in OSA detection.
    • The system demonstrated superior performance compared to existing methods.
    • The parallel processing of 1-D and 2-D HRV data improved diagnostic accuracy.

    Conclusions:

    • The developed system offers a novel and accurate approach for obstructive sleep apnea (OSA) recognition.
    • The integration of IoMT and deep learning provides a promising direction for remote OSA diagnosis.
    • Heart rate variability analysis, combined with deep learning, is effective for OSA detection.