Automatic decomposition of selective needle-detected myoelectric signals

A procedure for the storage and documentation of myoelectric signals has been developed that consists of a selective needle signal detection protocol, a data collection-compression routine, an adaptive signal decomposition algorithm, and an error filter. The collection-compression routine stores onl...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering Vol. 35; no. 1; pp. 1 - 10
Main Authors: Stashuk, D., De Bruin, H.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.01.1988
Institute of Electrical and Electronics Engineers
Subjects:
Action Potentials
Adaptive filters
Algorithms
Biological and medical sciences
Documentation
Electrodes
Electrodiagnosis. Electric activity recording
Electromyography - methods
Error analysis
Humans
Investigative techniques, diagnostic techniques (general aspects)
Medical sciences
Miscellaneous. Technology
Muscle Contraction
Needles
Protocols
Sampling methods
Signal detection
Signal Processing, Computer-Assisted
Signal resolution
Statistics
Electrical activity
Decomposition
Selectivity
Algorithm
Motor unit
Storage
Computerized processing
Automatic system
Signal processing
Electromyography
Physiology
Signal detection
Biomedical engineering
ISSN:0018-9294
Online Access:Get full text
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Summary:A procedure for the storage and documentation of myoelectric signals has been developed that consists of a selective needle signal detection protocol, a data collection-compression routine, an adaptive signal decomposition algorithm, and an error filter. The collection-compression routine stores only fixed-length signal epochs that contain motor unit action potentials (MUAPs) detected during individual motor unit firings. The decomposition algorithm assigns the collected MUAPs to candidate motor units, based on template matching using power-spectrum domain features and firing-time criteria calculated from the motor units' firing statistics. Power spectrum features allow the use of Nyquist sampling rates and remove the need for template alignment. The algorithm is adaptive and attempts to minimize dependent errors. The error filter, using firing statistics, accounts for unresolved superpositions and other decomposition errors. Using a standard TECA single-fiber needle electrode, signal recorded during isometric, constant, or slow force-varying contractions of up to 50% of the maximal voluntary contraction level, have been successfully analyzed.< >
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ISSN:0018-9294
DOI:10.1109/10.1330