Imperial College London
Alternate

ProfessorEmm MicDrakakis

Faculty of Engineering, Department of Bioengineering

Professor of Bio-Circuits and Systems
 
 
 
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Contact

 

e.drakakis Website

 
 
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Location

 

B207Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bashford:2020:10.1016/j.clinph.2019.09.015,
author = {Bashford, J and Wickham, A and Iniesta, R and Drakakis, E and Boutelle, M and Mills, K and Shaw, CE},
doi = {10.1016/j.clinph.2019.09.015},
journal = {Clinical Neurophysiology},
pages = {265--273},
title = {Preprocessing surface EMG data removes voluntary muscle activity and enhances SPiQE fasciculation analysis},
url = {http://dx.doi.org/10.1016/j.clinph.2019.09.015},
volume = {131},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - ObjectivesFasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). The Surface Potential Quantification Engine (SPiQE) is a novel analytical tool to identify fasciculation potentials from high-density surface electromyography (HDSEMG). This method was accurate on relaxed recordings amidst fluctuating noise levels. To avoid time-consuming manual exclusion of voluntary muscle activity, we developed a method capable of rapidly excluding voluntary potentials and integrating with the established SPiQE pipeline.MethodsSix ALS patients, one patient with benign fasciculation syndrome and one patient with multifocal motor neuropathy underwent monthly thirty-minute HDSEMG from biceps and gastrocnemius. In MATLAB, we developed and compared the performance of four Active Voluntary IDentification (AVID) strategies, producing a decision aid for optimal selection.ResultsAssessment of 601 one-minute recordings permitted the development of sensitive, specific and screening strategies to exclude voluntary potentials. Exclusion times (0.2–13.1 minutes), processing times (10.7–49.5 seconds) and fasciculation frequencies (27.4–71.1 per minute) for 165 thirty-minute recordings were compared. The overall median fasciculation frequency was 40.5 per minute (10.6–79.4 IQR).ConclusionWe hereby introduce AVID as a flexible, targeted approach to exclude voluntary muscle activity from HDSEMG recordings.SignificanceLongitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.
AU  - Bashford,J
AU  - Wickham,A
AU  - Iniesta,R
AU  - Drakakis,E
AU  - Boutelle,M
AU  - Mills,K
AU  - Shaw,CE
DO  - 10.1016/j.clinph.2019.09.015
EP  - 273
PY  - 2020///
SN  - 1388-2457
SP  - 265
TI  - Preprocessing surface EMG data removes voluntary muscle activity and enhances SPiQE fasciculation analysis
T2  - Clinical Neurophysiology
UR  - http://dx.doi.org/10.1016/j.clinph.2019.09.015
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000502603500036&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.sciencedirect.com/science/article/pii/S1388245719312465?via%3Dihub
UR  - http://hdl.handle.net/10044/1/77338
VL  - 131
ER  -
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