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Preprocessing surface EMG data removes voluntary muscle activity and enhances SPiQE fasciculation analysis

Research output: Contribution to journalArticlepeer-review

James Bashford, A. Wickham, R. Iniesta, M. Boutelle, K. Mills, CE. Shaw

Original languageEnglish
Pages (from-to)265-273
Number of pages9
JournalClinical Neurophysiology
Volume131
Issue number1
Early online date4 Nov 2019
DOIs
Accepted/In press23 Sep 2019
E-pub ahead of print4 Nov 2019

King's Authors

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.

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