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Related Concept Videos

Sleep Apnea01:21

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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...
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Audio-based snore detection using deep neural networks.

Jiali Xie1, Xavier Aubert1, Xi Long2

  • 1Biomedical Diagnostics Group, Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.

Computer Methods and Programs in Biomedicine
|January 12, 2021
PubMed
Summary
This summary is machine-generated.

A new algorithm accurately detects snoring using a convolutional neural network (CNN) and recurrent neural network (RNN). This method aids in screening for obstructive sleep apnea (OSA) and shows microphone placement has minimal impact on performance.

Keywords:
Audio signal processingBody-position in sleepConstant Q transformationConvolutional neural networkRecurrent neural networkSnore detection

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Area of Science:

  • Biomedical Engineering
  • Sleep Medicine
  • Artificial Intelligence

Background:

  • Snoring is common and can indicate obstructive sleep apnea (OSA).
  • Accurate snoring detection is crucial for OSA screening and diagnosis.

Purpose of the Study:

  • To develop and evaluate a novel algorithm for accurate snore detection.
  • To assess the impact of microphone placement on snore detection performance.

Main Methods:

  • A hybrid CNN-RNN model was employed for snore detection.
  • Audio recordings from 38 subjects with 5 strategically placed microphones were used.
  • CNN extracted features from spectrograms; RNN classified snore events.

Main Results:

  • The algorithm achieved high accuracy (95.3% ± 0.5%), sensitivity (92.2% ± 0.9%), and specificity (97.7% ± 0.4%).
  • Optimal performance was observed with a microphone positioned 70 cm above the subject.
  • Microphone placement variation showed minor differences in detection accuracy.

Conclusions:

  • The CNN-RNN algorithm effectively detects snoring events with high accuracy.
  • Microphone placement has a negligible effect on the overall performance of the snore detection system.