A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients

This paper presents a method to decompose multichannel long-term intramuscular electromyogram (EMG) signals. In contrast to existing decomposition methods which only support short registration periods or single-channel recordings of signals of constant muscle effort, the decomposition software EMG-L...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2003-01, Vol.50 (1), p.58-69
Main Authors: Zennaro, D., Wellig, P., Koch, V.M., Moschytz, G.S., Laubli, T.
Format: Article
Language:English
Subjects:
Action Potentials
Adult
Algorithm design and analysis
Algorithms
Biological and medical sciences
Cluster Analysis
Clustering algorithms
Computer Simulation
Decomposition
Diagnosis, Computer-Assisted - methods
Electromyography
Electromyography - methods
False Negative Reactions
False Positive Reactions
Female
Fingers - physiopathology
Humans
Internet
Male
Medical sciences
Middle Aged
Models, Neurological
Monitoring, Ambulatory - methods
Motor Neurons - classification
Movement
Muscle, Skeletal - physiopathology
Muscles
Musculoskeletal Diseases - physiopathology
Pattern Recognition, Automated
Porphyrins
Recruitment
Reproducibility of Results
Shape
Shoulder - physiopathology
Signal design
Signal Processing, Computer-Assisted
Software
Software packages
Studies
Wavelet analysis
Wavelet coefficients
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a method to decompose multichannel long-term intramuscular electromyogram (EMG) signals. In contrast to existing decomposition methods which only support short registration periods or single-channel recordings of signals of constant muscle effort, the decomposition software EMG-LODEC (ElectroMyoGram LOng-term DEComposition) is especially designed for multichannel long-term recordings of signals of slight muscle movements. A wavelet-based, hierarchical cluster analysis algorithm estimates the number of classes [motor units (MUs)], distinguishes single MUAPs from superpositions, and sets up the shape of the template for each class. Using three channels and a weighted averaging method to track action potential (AP) shape changes improve the analysis. In the last step, nonclassified segments, i.e., segments containing superimposed APs, are decomposed into their units using class-mean signals. Based on experiments on simulated and long-term recorded EMG signals, our software is capable of providing reliable decompositions with satisfying accuracy. EMG-LODEC is suitable for the study of MU discharge patterns and recruitment order in healthy subjects and patients during long-term measurements.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2002.807321