Preprocessing surface EMG data removes voluntary muscle activity and enhances SPiQE fasciculation analysis. Issue 1 (January 2020) - Bashford, J.; Wickham, A.; Iniesta, R.; Drakakis, E.; Boutelle, M.; Mills, K.; Shaw, CE.
Highlights: A novel preprocessing step removes the need for manual selection of relaxed surface EMG data. SPiQE provides reliable fasciculation analysis from raw thirty-minute recordings in ALS. This paves the way for clinical calibration of a potential novel biomarker of disease progression. 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.