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Pharyngeal wall vibration detection using an artificial neural network

K Behbehani1, F Lopez, F C Yen

  • 1Biomedical Engineering, University of Texas, Arlington, USA. kb@uta.edu

Medical & Biological Engineering & Computing
|May 1, 1997
PubMed
Summary

An artificial neural network accurately detects pharyngeal wall vibrations (PWV), a key indicator of obstructive sleep apnea (OSA). This technology enhances sleep apnea therapy by enabling automatic adjustments in continuous positive airway pressure (CPAP).

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Sleep Medicine

Background:

  • Obstructive sleep apnea (OSA) affects numerous adults, necessitating effective therapeutic interventions.
  • Continuous positive airway pressure (CPAP) is a primary treatment, but its efficacy can be improved with personalized pressure adjustments.
  • Pharyngeal wall vibration (PWV) is a physiological signal that precedes OSA events, offering a potential target for automated therapy modulation.

Purpose of the Study:

  • To develop and evaluate an artificial neural network (ANN) for automated detection of pharyngeal wall vibrations (PWV).
  • To assess the potential of ANN-based PWV detection in enhancing automatic positive airway pressure (APAP) therapy for OSA patients.

Main Methods:

  • An artificial neural network with 15 inputs, one output, and two hidden layers (each with two Adaline-nodes) was designed for PWV detection.

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  • The ANN was trained using nasal mask pressure data from five diagnosed OSA patients.
  • The detector's performance was validated on data from five independent OSA patients.

    • Main Results:

    • The ANN-based detector achieved an average accuracy of approximately 92% in identifying PWV events.
    • The system demonstrated a high accuracy of approximately 98% in correctly distinguishing normal breathing patterns.
    • The detector's accuracy remained consistent regardless of the therapeutic pressure levels used.

    Conclusions:

    • An ANN-based system can reliably detect pharyngeal wall vibrations (PWV), a precursor to obstructive sleep apnea (OSA) events.
    • This automated detection method holds significant promise for advancing continuous positive airway pressure (CPAP) therapy towards more personalized automatic positive airway pressure (APAP) systems.
    • The robustness of the ANN detector across different pressure levels suggests its practical applicability in real-world sleep apnea management.