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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Snoring detection using a piezo snoring sensor based on hidden Markov models.

Hyo-Ki Lee1, Jeon Lee, Hojoong Kim

  • 1Department of Biomedical Engineering, Yonsei University, Wonju, Gangwondo, Korea.

Physiological Measurement
|April 17, 2013
PubMed
Summary
This summary is machine-generated.

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A new method uses hidden Markov models (HMMs) and a neck-worn piezo sensor for accurate snoring detection. This approach offers a simpler, portable alternative to traditional microphone-based systems for identifying snoring, a key symptom of obstructive sleep apnea (OSA).

Area of Science:

  • Biomedical Engineering
  • Sleep Medicine
  • Signal Processing

Background:

  • Snoring is a primary indicator of obstructive sleep apnea (OSA).
  • Current microphone-based snoring detection requires high sampling rates and large data processing.
  • Automatic snoring detection using piezo sensors is an emerging area with limited research.

Purpose of the Study:

  • To develop and validate an automatic snoring detection method using a piezo snoring sensor.
  • To investigate the efficacy of hidden Markov models (HMMs) for analyzing piezo sensor data.
  • To provide a user-friendly and portable alternative to existing snoring detection techniques.

Main Methods:

  • Utilized a piezo snoring sensor attached to the neck.
  • Applied hidden Markov models (HMMs) for data classification.

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  • Computed short-time Fourier transform and short-time energy for feature extraction.
  • Trained and tested the model on data from 21 patients with OSA.

    • Main Results:

    • Achieved high sensitivity (93.3%) and positive predictivity (99.1%) for snoring detection.
    • Successfully classified data into snoring, noise, and silence categories using HMMs.
    • Demonstrated the feasibility of using piezo sensor data with HMMs for snoring analysis.

    Conclusions:

    • The proposed HMM-based method offers an effective approach for snoring detection using piezo sensors.
    • This method provides a simple, portable, and user-friendly tool for identifying snoring.
    • Presents a viable alternative to conventional microphone-based snoring detection systems.