Evidence map›Paper›PMID 22260842›Full record

Trial reportPhysiological measurement2012

Preferred sensor sites for surface EMG signal decomposition.

Farah Zaheer, Serge H Roy, Carlo J De Luca

Open access · greenAbstract readClinical Trial
In one paragraph

Trial report in Physiological measurement, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
1.7field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 76 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Farah ZaheerNeuroMuscular Research Center, Boston University, Boston, MA 02215, USA. farahz@bu.edu
Serge H Roy
Carlo J De Luca
Boston University · US

Funding

Harnessing Motoneuron Activity: From Lab to ClinicR01HD050111 · NICHD · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI DE LUCA, CARLO J · 2007 to 2011
$2.8M
NICHD NIH HHS HD050111NICHD NIH HHS R01 HD050111
6 · The paper itself

Abstract

Technologies for decomposing the electromyographic (EMG) signal into its constituent motor unit action potential trains have become more practical by the advent of a non-invasive methodology using surface EMG (sEMG) sensors placed on the skin above the muscle of interest (De Luca et al 2006 J. Neurophysiol. 96 1646-57 and Nawab et al 2010 Clin. Neurophysiol. 121 1602-15). This advancement has widespread appeal among researchers and clinicians because of the ease of use, reduced risk of infection, and the greater number of motor unit action potential trains obtained compared to needle sensor techniques. In this study we investigated the influence of the sensor site on the number of identified motor unit action potential trains in six lower limb muscles and one upper limb muscle with the intent of locating preferred sensor sites that provided the greatest number of decomposed motor unit action potential trains, or motor unit yield. Sensor sites rendered varying motor unit yields throughout the surface of a muscle. The preferred sites were located between the center and the tendinous areas of the muscle. The motor unit yield was positively correlated with the signal-to-noise ratio of the detected sEMG. The signal-to-noise ratio was inversely related to the thickness of the tissue between the sensor and the muscle fibers. A signal-to-noise ratio of 3 was found to be the minimum required to obtain a reliable motor unit yield.

Indexed as

Signal Processing, Computer-AssistedAction PotentialsAdolescentElectrodesElectromyographyHumansMotor NeuronsMuscle ContractionMusclesRegression AnalysisSignal-To-Noise RatioSkinfold ThicknessSurface PropertiesYoung Adult

Identifiers

PMID22260842
PMCPMC3428954
OpenAlexW2126101657

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.