6 results found
Bashford J, Masood U, Wickham A, et al., 2020, Fasciculations demonstrate daytime consistency in amyotrophic lateral sclerosis, Muscle and Nerve, Vol: 61, Pages: 745-750, ISSN: 0148-639X
IntroductionFasciculations represent early neuronal hyperexcitability in amyotrophic lateral sclerosis (ALS). To aid calibration as a disease biomarker, we set out to characterize the daytime variability of fasciculation firing.MethodsFasciculation awareness scores were compiled from 19 ALS patients. In addition, 10 ALS patients prospectively underwent high‐density surface electromyographic (HDSEMG) recordings from biceps and gastrocnemius at three time‐points during a single day.ResultsDaytime fasciculation awareness scores were low (mean: 0.28 muscle groups), demonstrating significant variability (coefficient of variation: 303%). Biceps HDSEMG recordings were highly consistent for fasciculation potential frequency (intraclass correlation coefficient [ICC] = 95%, n = 19) and the interquartile range of fasciculation potential amplitude (ICC = 95%, n = 19). These parameters exhibited robustness to observed fluctuations in data quality parameters. Gastrocnemius demonstrated more modest levels of consistency overall (44% to 62%, n = 20).DiscussionThere was remarkable daytime consistency of fasciculation firing in the biceps of ALS patients, despite sparse and intermittent awareness among patients’ accounts.
Bashford J, Wickham A, Iniesta R, et al., 2020, SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis (vol 130, pg 1083, 2019), Clinical Neurophysiology, Vol: 131, Pages: 350-350, ISSN: 1388-2457
[Correction to https://doi.org/10.1016/j.clinph.2019.03.032]
Bashford J, Wickham A, Iniesta R, et al., 2020, Preprocessing surface EMG data removes voluntary muscle activity and enhances SPiQE fasciculation analysis, Clinical Neurophysiology, Vol: 131, Pages: 265-273, ISSN: 1388-2457
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.
Bashford JA, Wickham A, Iniesta R, et al., 2020, The rise and fall of fasciculations in amyotrophic lateral sclerosis, BRAIN COMMUNICATIONS, Vol: 2
Bashford J, Wickham A, Iniesta R, et al., 2019, SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis, Clinical Neurophysiology, Vol: 130, Pages: 1083-1090, ISSN: 1388-2457
OBJECTIVES: Fasciculations are a clinical hallmark of amyotrophic lateral sclerosis (ALS). Compared to concentric needle EMG, high-density surface EMG (HDSEMG) is non-invasive and records fasciculation potentials (FPs) from greater muscle volumes over longer durations. To detect and characterise FPs from vast data sets generated by serial HDSEMG, we developed an automated analytical tool. METHODS: Six ALS patients and two control patients (one with benign fasciculation syndrome and one with multifocal motor neuropathy) underwent 30-minute HDSEMG from biceps and gastrocnemius monthly. In MATLAB we developed a novel, innovative method to identify FPs amidst fluctuating noise levels. One hundred repeats of 5-fold cross validation estimated the model's predictive ability. RESULTS: By applying this method, we identified 5,318 FPs from 80 minutes of recordings with a sensitivity of 83.6% (+/- 0.2 SEM), specificity of 91.6% (+/- 0.1 SEM) and classification accuracy of 87.9% (+/- 0.1 SEM). An amplitude exclusion threshold (100 μV) removed excessively noisy data without compromising sensitivity. The resulting automated FP counts were not significantly different to the manual counts (p = 0.394). CONCLUSION: We have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE). SIGNIFICANCE: Longitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.
Booth MA, Gowers SAN, Leong CL, et al., 2017, Chemical Monitoring in Clinical Settings: Recent Developments toward Real-Time Chemical Monitoring of Patients., Analytical Chemistry, Vol: 90, Pages: 2-18, ISSN: 0003-2700
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