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Feature extraction for snore sound via neural network processing.

T Emoto1, U R Abeyratne, M Akutagawa

  • 1Department of Electrical and Computer Engineering, Takamatsu National College of Technology, Takamatsu, Japan. emoto@takamatsu-nct.ac.jp

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
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Snoring sound analysis using neural networks can reveal hidden information about Obstructive Sleep Apnea (OSA). This method models snore sounds (SS) to better understand upper airway conditions during sleep.

Area of Science:

  • Biomedical Engineering
  • Sleep Medicine
  • Artificial Intelligence

Background:

  • Snore sound (SS) is a primary symptom of Obstructive Sleep Apnea (OSA).
  • OSA is a serious condition resulting from upper airway collapse during sleep.
  • Analyzing SS is complex despite its rich features and ease of acquisition.

Purpose of the Study:

  • To develop a novel method for analyzing snore sounds.
  • To investigate the potential of neural networks in modeling SS for OSA diagnosis.
  • To explore hidden features within SS related to upper airway status.

Main Methods:

  • Utilized a neural network (NN) based approach.
  • Employed a second-order one-step predictor for SS modeling.
  • Applied supervised training to capture SS features in the NN's connection weight space (CWS).

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  • Used independent component analysis (ICA) to validate the method's effectiveness.

    • Main Results:

    • Demonstrated that NN-based modeling can effectively capture hidden information from SS.
    • Showcased that SS features can be represented in the CWS of the trained NN.
    • Confirmed the availability and utility of the proposed method through ICA.

    Conclusions:

    • Neural network analysis offers a promising avenue for extracting valuable information from snore sounds.
    • The connection weight space of NNs can serve as a repository for SS-related features.
    • This approach may lead to improved diagnostic tools for Obstructive Sleep Apnea.