Evidence map›Paper›PMID 25540220›Full record

ArticleJournal of neurophysiology2015

Decomposition of surface EMG signals from cyclic dynamic contractions.

Carlo J De Luca, Shey-Sheen Chang, Serge H Roy, Joshua C Kline, S Hamid Nawab

Registry-linked trialOpen access · greenAbstract read
In one paragraph

Article in Journal of neurophysiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06516770 (The Effects of Different Types of Stimulation on Movement Control and Motor Unit Behavior in Individuals With Lumbopelvic Movement Control Impairment), which is not on this map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
2.9field-weighted citation impact, top 9% 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.

NCT06516770 naactive not recruitingnot on this mapstarted 2024, after this paper: background citation

The Effects of Different Types of Stimulation on Movement Control and Motor Unit Behavior in Individuals With Lumbopelvic Movement Control Impairment

TypeinterventionalSponsorMahidol UniversityRan2024 to 2026Enrolled136ConditionsLow Back PainArmsTranscranial direct current stimulation, Neuromuscular electrical stimulation, Motor control exercise, Isometric exercise
3 · Its place in the literature

Who cites it

47 citing papers in PubMed, 131 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

5 authors at 2 institutions in 1 country.

Carlo J De LucaNeuroMuscular Research Center, Boston University, Boston, Massachusetts; Department of Biomedical Engineering, Boston University, Boston, Massachusetts; Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts; Department of Neurology, Boston University, Boston, Massachusetts; Department of Physical Therapy, Boston University, Boston, Massachusetts; and Delsys, Natick, Massachusetts cjd@bu.edu.
Shey-Sheen ChangDelsys, Natick, Massachusetts.
Serge H RoyNeuroMuscular Research Center, Boston University, Boston, Massachusetts; Department of Physical Therapy, Boston University, Boston, Massachusetts; and.
Joshua C KlineNeuroMuscular Research Center, Boston University, Boston, Massachusetts;
S Hamid NawabDepartment of Biomedical Engineering, Boston University, Boston, Massachusetts; Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts;
Boston University · USDelsys (United States) · US

Funding

Non-invasive System for Identifying Motoneuron BehaviorR44NS077526 · NINDS · ALTEC, INC. · PI DE LUCA, GIANLUCA · 2014 to 2020
$3.5M
NINDS NIH HHS 2-R44-NS-077526-03NINDS NIH HHS R44 NS077526
6 · The paper itself

Abstract

Over the past 3 decades, various algorithms used to decompose the electromyographic (EMG) signal into its constituent motor unit action potentials (MUAPs) have been reported. All are limited to decomposing EMG signals from isometric contraction. In this report, we describe a successful approach to decomposing the surface EMG (sEMG) signal collected from cyclic (repeated concentric and eccentric) dynamic contractions during flexion/extension of the elbow and during gait. The increased signal complexity introduced by the changing shapes of the MUAPs due to relative movement of the electrodes and the lengthening/shortening of muscle fibers was managed by an incremental approach to enhancing our established algorithm for decomposing sEMG signals obtained from isometric contractions. We used machine-learning algorithms and time-varying MUAP shape discrimination to decompose the sEMG signal from an increasingly challenging sequence of pseudostatic and dynamic contractions. The accuracy of the decomposition results was assessed by two verification methods that have been independently evaluated. The firing instances of the motor units had an accuracy of ∼90% with a MUAP train yield as high as 25. Preliminary observations from the performance of motor units during cyclic contractions indicate that during repetitive dynamic contractions, the control of motor units is governed by the same rules as those evidenced during isometric contractions. Modifications in the control properties of motoneuron firings reported by previous studies were not confirmed. Instead, our data demonstrate that the common drive and hierarchical recruitment of motor units are preserved during concentric and eccentric contractions.

Indexed as

Isometric ContractionMachine LearningAdultArmElectromyographyFemaleHumansMaleMiddle AgedPeriodicitydynamic contractionsfiring rategaitmotor units

Identifiers

PMID25540220
PMCPMC4359986
OpenAlexW2156525434

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.