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

McCaughey, E.J., McLachlan, A.J. and Gollee, H. (2014) Non-intrusive real-time breathing pattern detection and classification for automatic abdominal functional electrical stimulation. Medical Engineering and Physics, 36(8), pp. 1057-1061. (doi:10.1016/j.medengphy.2014.04.005)

[img]
Preview
Text
98492.pdf - Accepted Version

368kB

Abstract

Abdominal Functional Electrical Stimulation (AFES) has been shown to improve the respiratory function of people with tetraplegia. The effectiveness of AFES can be enhanced by using different stimulation parameters for quiet breathing and coughing. The signal from a spirometer, coupled with a facemask, has previously been used to differentiate between these breath types. In this study, the suitability of less intrusive sensors was investigated with able-bodied volunteers. Signals from two respiratory effort belts, positioned around the chest and the abdomen, were used with a Support Vector Machine (SVM) algorithm, trained on a participant by participant basis, to classify, in real-time, respiratory activity as either quiet breathing or coughing. This was compared with the classification accuracy achieved using a spirometer signal and an SVM. The signal from the belt positioned around the chest provided an acceptable classification performance compared to the signal from a spirometer (mean cough (<i>c</i>) and quiet breath (<i>q</i>) sensitivity (<i>Se</i>) of <i>Se<sup>c</sup></i> = 92.9% and <i>Se<sup>q</sup></i> = 96.1% vs. <i>Se<sup>c</sup></i> = 90.7% and <i>Se<sup>q</sup></i> = 98.9%). The abdominal belt and a combination of both belt signals resulted in lower classification accuracy. We suggest that this novel SVM classification algorithm, combined with a respiratory effort belt, could be incorporated into an automatic AFES device, designed to improve the respiratory function of the tetraplegic population.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Gollee, Dr Henrik and McCaughey, Mr Euan and McLachlan, Mr Angus
Authors: McCaughey, E.J., McLachlan, A.J., and Gollee, H.
College/School:College of Science and Engineering > School of Engineering > Biomedical Engineering
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Medical Engineering and Physics
Publisher:Elsevier Ltd.
ISSN:1350-4533
ISSN (Online):1873-4030
Copyright Holders:Copyright © 2014 Elsevier
First Published:First published in Medical Engineering and Physics 36(8):1057-1061
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

University Staff: Request a correction | Enlighten Editors: Update this record