Preprocessing surface EMG data removes voluntary muscle activity and enhances SPiQE fasciculation analysis
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Objectives
Fasciculations 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.
Methods
Six 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.
Results
Assessment 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).
Conclusion
We hereby introduce AVID as a flexible, targeted approach to exclude voluntary muscle activity from HDSEMG recordings.
Significance
Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.
Fasciculations 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.
Methods
Six 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.
Results
Assessment 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).
Conclusion
We hereby introduce AVID as a flexible, targeted approach to exclude voluntary muscle activity from HDSEMG recordings.
Significance
Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.
Date Issued
2020-01-01
Date Acceptance
2019-09-23
Citation
Clinical Neurophysiology, 2020, 131 (1), pp.265-273
ISSN
1388-2457
Publisher
Elsevier
Start Page
265
End Page
273
Journal / Book Title
Clinical Neurophysiology
Volume
131
Issue
1
Copyright Statement
© 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000502603500036&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
EP/K503381/1
Subjects
Science & Technology
Life Sciences & Biomedicine
Clinical Neurology
Neurosciences
Neurosciences & Neurology
Amyotrophic lateral sclerosis
Fasciculation
High-density surface EMG
Biomarker
Motor unit
AMYOTROPHIC-LATERAL-SCLEROSIS
MOTOR-NEURON DISEASE
ALS
MANAGEMENT
POTENTIALS
DIAGNOSIS
FEATURES
Publication Status
Published
Date Publish Online
2019-11-04