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

TY - JOUR

T1 - Preprocessing surface EMG data removes voluntary muscle activity and enhances SPiQE fasciculation analysis

AU - Bashford, James

AU - Wickham, A.

AU - Iniesta, R.

AU - Boutelle, M.

AU - Mills, K.

AU - Shaw, CE.

PY - 2019/11/4

Y1 - 2019/11/4

N2 - 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.

AB - 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.

KW - Amyotrophic lateral sclerosis

KW - Biomarker

KW - Fasciculation

KW - High-density surface EMG

KW - Motor unit

UR - http://www.scopus.com/inward/record.url?scp=85075459194&partnerID=8YFLogxK

U2 - 10.1016/j.clinph.2019.09.015

DO - 10.1016/j.clinph.2019.09.015

M3 - Article

SN - 1388-2457

VL - 131

SP - 265

EP - 273

JO - Clinical Neurophysiology

JF - Clinical Neurophysiology

IS - 1

ER -