𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Multivariate analysis of diaphragm EMG power spectral moments

✍ Scribed by J.Milton Adams; T.K. Aldrich; N.S. Arora; D.F. Rochester


Publisher
Elsevier Science
Year
1984
Tongue
English
Weight
686 KB
Volume
17
Category
Article
ISSN
0010-4809

No coin nor oath required. For personal study only.

✦ Synopsis


A single derived index of the power spectrum of the diaphragm electromyogram (EMG) has been used in detecting fatigue. Additional information in the EMG could be used to study diaphragm function in other respiratory conditions. Diaphragm EMGs and calculated power spectra at 12 frequencies were measured in normal subjects and patients with severe chronic obstructive pulmonary disease during several respiratory maneuvers both before and after treadmill exercise to dyspnea. The power spectra were characterized by the first five moments. Changes in the EMG were similar when assessed by multivariate analysis of variance of the spectral estimates or of the moments. Factor analysis provided two latent variables that correlated with the first and second moment respectively. The first moment was found to be the most sensitive single discriminant of fatigue and is only slightly improved by adding other information. It is concluded that the first and second moments of the EMG power spectra provide a concise, parsimonious description of the changes in the EMG.


πŸ“œ SIMILAR VOLUMES


Needle EMG of the human diaphragm: Power
✍ Robert Chen; Stephen J. Collins; Hussein Remtulla; Anthony Parkes; Charles F. Bo πŸ“‚ Article πŸ“… 1996 πŸ› John Wiley and Sons 🌐 English βš– 641 KB

Needle EMG of the diaphragm was performed in 43 diaphragms in 23 healthy volunteers. The mean -rstandard deviation for the median frequency (MF) of the power spectrum was 233.3 5 58.1 Hz. The MF increased with age and showed a negative correlation with the forced vital capacity (FVC), but there was

Multivariate analysis of time-resolved m
✍ W. Windig; T. Chakravarty; J.M. Richards; H.L.C. Meuzelaar πŸ“‚ Article πŸ“… 1986 πŸ› Elsevier Science 🌐 English βš– 852 KB

Multivariate analysis of time-resolved pyrolysis/mass spectrometric data is described. The approach is based on the variance diagram (VARDIA), a recently developed technique that quantifies the clustering of variables in two-dimensional factor analysis (sub)spaces in a rotational scanning procedure.