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Non-intrusive real-time breathing pattern detection and classification for automatic abdominal functional electrical

E J McCaughey1, A J McLachlan1, H Gollee1

  • 1Centre for Rehabilitation Engineering, University of Glasgow, University Avenue, Glasgow G12 8QQ, UK.

Medical Engineering & Physics
|June 5, 2014
PubMed
Summary

Respiratory effort belts effectively distinguish breathing types for improved functional electrical stimulation in tetraplegia. This non-intrusive method enhances respiratory function by adapting stimulation for quiet breathing and coughing.

Keywords:
ClassifierControl systemElectrical stimulationRespiratory functionSpinal cord injuryTetraplegia

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Area of Science:

  • Biomedical Engineering
  • Respiratory Physiology
  • Rehabilitation Technology

Background:

  • Abdominal Functional Electrical Stimulation (AFES) aids respiratory function in tetraplegia.
  • Optimizing AFES requires differentiating between quiet breathing and coughing.
  • Current methods use spirometry, which can be intrusive.

Purpose of the Study:

  • To evaluate less intrusive respiratory effort belts for classifying breathing patterns.
  • To compare belt sensor accuracy with spirometry for real-time breathing classification.
  • To assess the potential integration of belt sensors into automated AFES systems.

Main Methods:

  • Able-bodied volunteers used chest and abdominal respiratory effort belts.
  • A Support Vector Machine (SVM) algorithm classified breathing as quiet or coughing.
  • Classification accuracy was compared between belts and a spirometer.

Main Results:

  • The chest belt achieved high classification accuracy (Se(c)=92.9%, Se(q)=96.1%), comparable to spirometry (Se(c)=90.7%, Se(q)=98.9%).
  • Abdominal belts and combined belts showed lower classification performance.
  • SVM algorithm demonstrated real-time classification capability.

Conclusions:

  • Respiratory effort belts, particularly chest placement, offer a viable alternative to spirometry for breathing pattern classification.
  • This technology could enable automated AFES devices for enhanced respiratory support in tetraplegia.
  • Further research may refine algorithms for improved accuracy and broader application.