Journal of Applied Physiology Fuel your research with LabChart
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


J Appl Physiol 57: 913-916, 1984;
8750-7587/84 $5.00
This Article
Right arrow Full Text (PDF)
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Bower, J. S.
Right arrow Articles by Dantzker, D. R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Bower, J. S.
Right arrow Articles by Dantzker, D. R.

Journal of Applied Physiology, Vol 57, Issue 3 913-916, Copyright © 1984 by American Physiological Society


ARTICLES

Time domain analysis of diaphragmatic electromyogram during fatigue in men

J. S. Bower, T. G. Sandercock, E. Rothman, P. H. Abbrecht and D. R. Dantzker

Diaphragmatic fatigue has been correlated with a change in the electromyogram recorded from the diaphragm (EMGdi), which suggests that the electromyogram is a potential clinical tool to detect respiratory muscle fatigue. Changes in the EMGdi have previously been quantified by using the power spectral parameters high-low ratio or mean frequency. In this study, we developed an autoregressive model of the EMG in an attempt to improve the analysis of the EMGdi. This model was tested on recordings of the EMGdi that were obtained from an esophageal electrode in five normal subjects breathing to fatigue through an inspiratory resistor. The data obtained from the autoregressive model were directly compared with data from the high-low ratio and mean frequency techniques. The autoregressive model showed an excellent correlation with mean frequency. Both techniques were superior to the high-low ratio measurement. Because the autoregressive model requires much less computation than mean frequency and can be easily implemented in real time on a minicomputer, we propose this as a preferable approach.





HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Visit Other APS Journals Online