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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.
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The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
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Updated: Nov 21, 2025

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Detection of Snore from OSAHS Patients Based on Deep Learning.

Fanlin Shen1, Siyi Cheng1, Zhu Li1

  • 1Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou, Zhejiang, China.

Journal of Healthcare Engineering
|January 18, 2021
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Summary

Obstructive sleep apnea-hypopnea syndrome (OSAHS) can be detected using snoring sounds. A new deep learning method analyzes snoring to identify OSAHS and its severity, offering convenient self-monitoring for patients.

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Area of Science:

  • Biomedical Engineering
  • Medical Informatics
  • Otolaryngology

Background:

  • Obstructive sleep apnea-hypopnea syndrome (OSAHS) poses significant health risks, affecting multiple organ systems.
  • Current diagnosis relies on polysomnography (PSG), which is not practical for daily monitoring.
  • Delayed OSAHS treatment can lead to severe neurological, endocrine, cardiovascular, kidney, and mental health issues.

Purpose of the Study:

  • To develop a convenient, self-monitoring method for recognizing OSAHS using snoring sounds.
  • To analyze and differentiate snoring sounds between healthy individuals and OSAHS patients.
  • To classify apnea-related snoring and determine OSAHS severity.

Main Methods:

  • Theoretical analysis of snoring sounds in time and frequency domains.
  • Feature extraction using Mel-frequency cepstral coefficients (MFCC), Linear Predictive Cepstral Coefficients (LPCC), and LPMFCC.
  • Classification of snoring sounds using deep learning models, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM).

Main Results:

  • The MFCC feature extraction method combined with the LSTM model achieved the highest accuracy of 87% for binary classification of snoring data.
  • The proposed algorithm can estimate the Apnea-Hypopnea Index (AHI) value.
  • The system effectively recognizes OSAHS and determines its severity degree.

Conclusions:

  • Snoring sound analysis using deep learning presents a viable method for OSAHS detection and severity assessment.
  • This approach offers a convenient self-monitoring tool for patients, potentially reducing delayed diagnosis and treatment.
  • The MFCC and LSTM combination demonstrates high efficacy in classifying apnea-related snoring sounds.