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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

252
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...
252

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A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems.

Anita Ramachandran1, Anupama Karuppiah2

  • 1Department of Computer Science & Information Systems, BITS, Pilani 560001, India.

Healthcare (Basel, Switzerland)
|August 6, 2021
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Summary

Diagnosing sleep apnea can be improved with machine learning and embedded systems. These technologies offer a more accessible and affordable alternative to the standard polysomnography (PSG) test for sleep apnea detection.

Keywords:
deep learningmachine learningsleep apneawearable systems

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Sleep Medicine

Background:

  • Sleep apnea is a prevalent sleep disorder linked to serious health issues like cardiovascular dysfunction, stroke, and diabetes.
  • Current gold standard diagnosis via polysomnography (PSG) is costly, inconvenient, and inaccessible to many.
  • There is a critical need for accessible and affordable sleep apnea diagnostic solutions.

Purpose of the Study:

  • To review clinical sleep apnea detection methods.
  • To explore the integration of embedded systems and machine learning for improved diagnosis.
  • To highlight recent advances in machine learning for sleep apnea detection.

Main Methods:

  • Literature review of machine learning, deep learning, and sensor fusion in sleep apnea detection.
  • Analysis of sensor types, feature engineering techniques, and classifiers used.
  • Examination of challenges in designing sleep apnea detection systems.

Main Results:

  • Machine learning, deep learning, and sensor fusion show promise for sleep apnea diagnosis.
  • Various sensors, feature engineering methods, and classifiers are employed in current research.
  • Key challenges include data acquisition, feature selection, and model generalization.

Conclusions:

  • Advances in embedded systems and machine learning can significantly enhance sleep apnea diagnosis accessibility and affordability.
  • Further research is needed to overcome design challenges and optimize detection systems.
  • The integration of these technologies offers a pathway to democratize sleep apnea diagnosis.