Clustering analysis and pattern discrimination of EMG linear envelopes

A technique has been developed for performing pattern analysis of electromyographic (EMG) activities generated during locomotion. It was found that the shapes of the EMG linear envelopes (LE) are mainly determined by their phase spectra; their magnitude spectra are much less important. Autoregressiv...

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Bibliographic Details
Published inIEEE transactions on biomedical engineering Vol. 38; no. 8; pp. 777 - 784
Main Authors Zhang, L.-Q., Shiavi, R., Hunt, M.A., Chen, J.-J.J.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.08.1991
Institute of Electrical and Electronics Engineers
Subjects
Anterior Cruciate Ligament Injuries
Biological and medical sciences
Cluster Analysis
Discrete Fourier transforms
Discrimination (Psychology) - physiology
Electrodiagnosis. Electric activity recording
Electromyography
Feature extraction
Fourier Analysis
Humans
Investigative techniques, diagnostic techniques (general aspects)
Knee Joint - physiology
Legged locomotion
Ligaments
Medical sciences
Movement - physiology
Nervous system
Parametric statistics
Pattern analysis
Power system harmonics
Reference Values
Shape
Testing
Walking
Linear form
Diseases of the osteoarticular system
Rupture
Discrete Fourier transformation
Nervous system
Autoregressive model
Striated muscle
Synergism
Anterior cruciate ligament
Knee disease
Electrodiagnosis
Locomotion
Walking
Signal envelope
Analysis method
Classification
Signal processing
Electromyography
Pattern analysis
Biomedical engineering
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ISSN0018-9294
DOI10.1109/10.83590

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Summary:A technique has been developed for performing pattern analysis of electromyographic (EMG) activities generated during locomotion. It was found that the shapes of the EMG linear envelopes (LE) are mainly determined by their phase spectra; their magnitude spectra are much less important. Autoregressive (AR) parametric models and discrete Fourier transform (DFT) approaches were tested and compared. The latter proved to be a better way to describe the EMG LEs. Feature extraction and clustering were performed by performing a DFT of EMG LEs, extracting part of the phase and magnitude spectra as features, and using the percent powers to weigh the corresponding harmonics. The approach was applied to the clustering analysis of EMG LEs of normal and anterior cruciate ligaments (ACL) injured subjects during walking.< >
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ISSN:0018-9294
DOI:10.1109/10.83590