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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

413
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...
413
Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Related Experiment Video

Updated: Dec 30, 2025

Multi-Modal Home Sleep Monitoring in Older Adults
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Development of a Sleep Apnea Detection Algorithm Using Long Short-Term Memory and Heart Rate Variability.

Ayako Iwasaki, Chikao Nakayama, Koichi Fujiwara

    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

    A new screening method for sleep apnea syndrome (SAS) uses heart rate variability (HRV) and AI to accurately detect the condition. This approach offers a simpler alternative to traditional polysomnography (PSG) for diagnosing sleep apnea.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence in Medicine
    • Sleep Medicine

    Background:

    • Sleep apnea syndrome (SAS) is a common disorder linked to fatigue and increased risk of chronic diseases.
    • Diagnosis of SAS is often challenging due to the complexity of the gold standard test, polysomnography (PSG).
    • Many patients remain undiagnosed and untreated, highlighting the need for accessible screening methods.

    Purpose of the Study:

    • To develop a simple and effective screening tool for sleep apnea syndrome.
    • To leverage heart rate variability (HRV) and advanced machine learning for SAS detection.
    • To provide a more accessible alternative to polysomnography (PSG) for sleep apnea screening.

    Main Methods:

    • Utilized heart rate variability (HRV) data as input for the screening algorithm.
    • Employed long short-term memory (LSTM) neural network techniques for data analysis.
    • Applied the developed algorithm to clinical data for validation.

    Main Results:

    • The proposed screening method achieved high accuracy in discriminating between patients with SAS and healthy individuals.
    • Demonstrated 100% sensitivity in identifying sleep apnea syndrome.
    • Achieved 100% specificity in distinguishing between patients and healthy controls.

    Conclusions:

    • The HRV and LSTM-based algorithm presents a highly sensitive and specific method for sleep apnea screening.
    • This novel approach offers a simpler and potentially more accessible alternative to polysomnography (PSG).
    • The findings suggest a promising tool for improving the diagnosis and management of sleep apnea syndrome.