DocumentCode :
1331881
Title :
Study of myographic signals from sternomastoid muscle in patients with chronic obstructive pulmonary disease
Author :
Mananas, M.A. ; Jané, Raimon ; Fiz, José Antonio ; Morera, Josep ; Caminal, Pere
Author_Institution :
ESAII Dept., Univ. Politecnica de Catalunya, Barcelona, Spain
Volume :
47
Issue :
5
fYear :
2000
fDate :
5/1/2000 12:00:00 AM
Firstpage :
674
Lastpage :
681
Abstract :
Analysis of the respiratory muscle activity is a promising technique for diagnosis of respiratory diseases, such as chronic obstructive pulmonary disease (COPD). The sternomastoid muscle (SMM) was selected to study the activity of respiratory muscles due to its accessibility in order to define a noninvasive analysis. The aims of this work are two: analyze the relationship between the SMM function and pulmonary obstruction, and study the influence of spectral estimator on frequency parameters related with the muscle activity. For the first goal, we propose the analysis of vibromyographic and electromyographic signals from the SMM to study the muscle function during two ventilatory tests. Activity of SMM was found by means of several indexes: root-mean-square (rms) values, mean and median frequencies, and ratio between high and low-frequency components. For the second goal, spectral analysis was performed by means of nonparametric methods: Correlogram and Welch periodogram, and parametric methods: autoregressive (AR), moving average (MA), and ARMA models. It is deduced that these indexes show muscle activity and certain fatigue of the SMM, whose muscle function depends on the level of pulmonary obstruction, and they depend a lot on spectral estimator being the more suitable an AR model with high order.
Keywords :
autoregressive moving average processes; autoregressive processes; diseases; electromyography; lung; medical signal processing; moving average processes; pneumodynamics; spectral analysis; ARMA model; Correlogram; Welch periodogram; autoregressive model; chronic obstructive pulmonary disease; electromyographic signals; fatigue; frequency parameters; high-frequency components; low-frequency components; mean frequencies; median frequencies; moving average model; muscle function; myographic signals; noninvasive analysis; nonparametric methods; parametric methods; patients; pulmonary obstruction; respiratory disease diagnosis; respiratory muscle activity; root-mean-square values; spectral analysis; spectral estimator; sternomastoid muscle; ventilatory tests; vibromyographic signals; Diseases; Electromyography; Fatigue; Frequency estimation; Life estimation; Muscles; Parameter estimation; Signal analysis; Spectral analysis; Testing; Aged; Electromyography; Humans; Linear Models; Lung Diseases, Obstructive; Male; Muscle Fatigue; Respiratory Muscles; Signal Processing, Computer-Assisted; Statistics, Nonparametric; Vibration;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/10.841339
Filename :
841339
Link To Document :
بازگشت