Abstract
ObjectivesFasciculations 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. MethodsSix 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. ResultsBy 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). ConclusionWe have devised and internally validated an automated method to accurately identify FPs from HDSEMG, a technique we have named Surface Potential Quantification Engine (SPiQE). SignificanceLongitudinal quantification of fasciculations in ALS could provide unique insight into motor neuron health.
Highlights
amyotrophic lateral sclerosis (ALS) is caused by the progressive dysfunction and death of motor neurons and affects ~1,200 people in the UK every year.(Al-Chalabi and Hardiman, 2013) It typically causes progressive paralysis and death within three to five years of symptom onset
SEMG has improved the detection rate of fasciculations compared to clinical examination or needle electromyography (NEMG).(Howard and Murray, 1992, Hjorth et al, 1973) The advent of high-density surface EMG (HDSEMG), where multiple channels are aligned in a linear or grid formation, has provided superior spatial resolution and muscle coverage compared to single channel SEMG.(van Dijk et al, 2010) Surface Potential Quantification Engine (SPiQE) combines this non-invasive sensor technology with an innovative signal-processing algorithm to quantify fasciculation potential (FP) in an accurate and automated way
We estimated the accuracy of the two models using receiver operating characteristic (ROC) analysis, adopted from its widely used role in clinical diagnostics.(Linden, 2006) To establish the ground truth, we developed a simple definition of a fasciculation potential for manual analysis, providing a standardised approach across all recordings
Summary
ALS is caused by the progressive dysfunction and death of motor neurons and affects ~1,200 people in the UK every year.(Al-Chalabi and Hardiman, 2013) It typically causes progressive paralysis and death within three to five years of symptom onset. There is only one licensed drug in Europe (riluzole) with modest survival benefit.(Bensimon et al, 1994) Drug trials in ALS are time-consuming for patients, expensive for funders and hampered by insensitive measurements of disease progression. A motor unit (MU) comprises the motor neuron cell body, axon, terminal branches and connecting muscle fibres. Ailing motor neurons are electrically unstable and spontaneously discharge electrical impulses causing fasciculation potentials (FPs).(de Carvalho and Swash, 2016b) Subsequently, the motor neuron becomes electrically unresponsive and dies, disrupting MU architecture through denervation. Orphaned muscle fibres can become re-innervated by sprouting motor axons. This process of denervation and reinnervation results in MU potentials becoming longer in duration with more complex morphologies over time.(Conradi et al, 1982, de Carvalho and Swash, 2013)
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