Background
Type: Article

Robust decomposition of single-channel intramuscular EMG signals at low force levels

Journal: Journal of Neural Engineering (17412552)Year: 2011Volume: Issue: 6
Marateb H.a Muceli S. McGill K.C. Merletti R. Farina D.
DOI:10.1088/1741-2560/8/6/066015Language: English

Abstract

This paper presents a density-based method to automatically decompose single-channel intramuscular electromyogram (EMG) signals into their component motor unit action potential (MUAP) trains. In contrast to most previous decomposition methods, which require pre-setting and (or) tuning of multiple parameters, the proposed method takes advantage of the data-dependent strategies in the pattern recognition procedures. In this method, outliers (superpositions) are excluded prior to classification and MUAP templates are identified by an adaptive density-based clustering procedure. MUAP trains are then identified by a novel density-based classifier that incorporates MUAP shape and discharge time information. MUAP trains are merged by a fuzzy system that incorporates expert human knowledge. Finally, superimpositions are resolved to fill the gaps in the MUAP trains. The proposed decomposition algorithm has been experimentally tested on signals from low-force (≤30% maximal) isometric contractions of the vastus medialis obliquus, vastus lateralis, biceps femoris long-head and tibialis anterior muscles. Comparison with expert manual decomposition that had been verified using a rigorous statistical analysis showed that the algorithm identified 80% of the total 229 motor unit trains with an accuracy greater than 90%. The algorithm is robust and accurate, and therefore it is a promising new tool for decomposing single-channel multi-unit signals. © 2011 IOP Publishing Ltd.


Other Keywords

Action PotentialsAdultElectromyographyHumansMaleMiddle AgedMuscle, SkeletalSignal TransductionYoung AdultElectromyographyJoints (anatomy)Pattern recognitionBiceps femorisDecomposition algorithmDecomposition methodsDensity-basedDensity-based ClusteringDensity-based methodDischarge timeElectromyogramEMG signalForce levelHuman knowledgeIsometric contractionsMotor unitMotor unit action potentialsMulti-unitMultiple parametersPattern recognition proceduresPre-settingSingle-channelTibialis anteriorVastus medialisaccuracyalgorithmarticleelectromyogramintramuscular electromyogrammotor unit potentialpattern recognitionpriority journalspikestatistical analysistibialis anterior musclevastus lateralis musclevastus medialis muscleaction potentialadultelectromyographyhumaninstrumentationmalemethodologymiddle agedphysiologysignal transductionskeletal muscleAlgorithms