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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

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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...
343

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Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network.

Hung-Chi Chang1, Hau-Tieng Wu2, Po-Chiun Huang1

  • 1Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.

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|October 29, 2020
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Summary
This summary is machine-generated.

A new wearable device and AI model accurately detect sleep apnea events at home. This system classifies breathing patterns, aiding in the diagnosis of obstructive sleep apnea/hypopnea syndrome (OSAHS).

Keywords:
Abdominal movement signalLSTM-RNNhypopneaneural networkoxygen saturationsleep apnea syndromesleep–wake detectionsynchrosqueezing transformthoracic movement signaltriaxial accelerometer

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Sleep Medicine

Background:

  • Obstructive sleep apnea/hypopnea syndrome (OSAHS) involves upper airway collapse during sleep, leading to hypoxia and cardiovascular risks.
  • Traditional polysomnography (PSG) is effective but impractical for home screening.
  • Developing accessible home-screening tools for OSAHS is crucial.

Purpose of the Study:

  • To develop and validate a novel wearable measuring module for home-based OSAHS screening.
  • To propose a long short-term memory (LSTM) recurrent neural network for classifying sleep breathing patterns.
  • To assess the system's accuracy against traditional PSG expert interpretation.

Main Methods:

  • A custom module integrated triaxial accelerometers, a pulse oximeter (SpO2), and an electrocardiogram sensor.
  • An LSTM recurrent neural network was trained to classify obstructive sleep apnea (OSA), central sleep apnea (CSA), hypopnea (HYP), and normal breathing (NOR).
  • The system calculated the apnea-hypopnea index (AHI) and identified specific event types, validated against PSG in 115 participants.

Main Results:

  • The system achieved 89.3% accuracy in AHI severity group classification.
  • The AHI difference compared to PSG expert interpretation was 5.0±4.5.
  • The overall accuracy for detecting abnormal OSA, CSA, and HYP events reached 92.3%.

Conclusions:

  • The proposed wearable system and LSTM algorithm offer a viable solution for home screening of OSAHS.
  • This technology can accurately identify and classify sleep-disordered breathing events, supporting clinical diagnosis.
  • The system demonstrates high performance, comparable to traditional PSG, for OSAHS assessment.