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

E J McCaughey, A J McLachlan, H Gollee

Research output: Contribution to journalArticle

5 Citations (Scopus)

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 (c) and quiet breath (q) sensitivity (Se) of Se(c)=92.9% and Se(q)=96.1% vs. Se(c)=90.7% and Se(q)=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.

LanguageEnglish
Pages1057-1061
Number of pages5
JournalMedical Engineering and Physics
Volume36
Issue number8
Early online date2 Jun 2014
DOIs
Publication statusPublished - Aug 2014

Fingerprint

Electric Stimulation
Respiration
Support vector machines
Thorax
Quadriplegia
Cough
Abdomen
Volunteers
Equipment and Supplies
Population
Sensors
Support Vector Machine

Keywords

  • electrical stimulation
  • respiratory function
  • tetraplegia
  • control system
  • spinal cord injury

Cite this

@article{59d2b67b380c44b19a00575260df8441,
title = "Non-intrusive real-time breathing pattern detection and classification for automatic abdominal functional electrical stimulation",
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 (c) and quiet breath (q) sensitivity (Se) of Se(c)=92.9{\%} and Se(q)=96.1{\%} vs. Se(c)=90.7{\%} and Se(q)=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.",
keywords = "electrical stimulation, respiratory function, tetraplegia, control system, spinal cord injury",
author = "McCaughey, {E J} and McLachlan, {A J} and H Gollee",
year = "2014",
month = "8",
doi = "10.1016/j.medengphy.2014.04.005",
language = "English",
volume = "36",
pages = "1057--1061",
journal = "Medical Engineering and Physics",
issn = "1350-4533",
number = "8",

}

Non-intrusive real-time breathing pattern detection and classification for automatic abdominal functional electrical stimulation. / McCaughey, E J; McLachlan, A J; Gollee, H.

In: Medical Engineering and Physics , Vol. 36, No. 8, 08.2014, p. 1057-1061.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Non-intrusive real-time breathing pattern detection and classification for automatic abdominal functional electrical stimulation

AU - McCaughey, E J

AU - McLachlan, A J

AU - Gollee, H

PY - 2014/8

Y1 - 2014/8

N2 - 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 (c) and quiet breath (q) sensitivity (Se) of Se(c)=92.9% and Se(q)=96.1% vs. Se(c)=90.7% and Se(q)=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.

AB - 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 (c) and quiet breath (q) sensitivity (Se) of Se(c)=92.9% and Se(q)=96.1% vs. Se(c)=90.7% and Se(q)=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.

KW - electrical stimulation

KW - respiratory function

KW - tetraplegia

KW - control system

KW - spinal cord injury

U2 - 10.1016/j.medengphy.2014.04.005

DO - 10.1016/j.medengphy.2014.04.005

M3 - Article

VL - 36

SP - 1057

EP - 1061

JO - Medical Engineering and Physics

T2 - Medical Engineering and Physics

JF - Medical Engineering and Physics

SN - 1350-4533

IS - 8

ER -