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Non-invasive measurement of fasciculation frequency demonstrates diagnostic performance in amyotrophic lateral sclerosis

Research output: Contribution to journalArticlepeer-review

Arina Tamborska, James Bashford, Aidan Wickham, Raquel Iniesta, Urooba Masood, Cristina Cabassi, Domen Planinc, Emma Hodson-Tole, Emmanuel Drakakis, Martyn Boutelle, Kerry Mills, Christopher Shaw

Original languageEnglish
JournalBrain Communications
Accepted/In press29 Jul 2020


  • BRAINCOM-2020-147.R1_Proof_fl

    BRAINCOM_2020_147.R1_Proof_fl.pdf, 1.19 MB, application/pdf

    Uploaded date:08 Sep 2020

    Version:Accepted author manuscript

King's Authors


Delayed diagnosis of amyotrophic lateral sclerosis prevents early entry into clinical trials at a time when neuroprotective therapies would be most effective. Fasciculations are an early hallmark of the amyotrophic lateral sclerosis, preceding muscle weakness and atrophy. To assess the potential diagnostic utility of fasciculations measured by high-density surface EMG, we carried out 30-minute biceps brachii recordings in 39 patients with amyotrophic lateral sclerosis, seven patients with benign fasciculation syndrome, one patient with multifocal motor neuropathy and 17 healthy individuals. We employed the Surface Potential Quantification Engine (SPiQE) to compute fasciculation frequency, fasciculation amplitude and inter-fasciculation interval. Inter-group comparison was assessed by Welch’s analysis of variance. Logistic regression, receiver operating characteristic curves and decision trees discerned the diagnostic performance of these measures. Fasciculation frequency, median fasciculation amplitude and proportion of inter-fasciculation intervals <100ms showed significant differences between the groups. In the best-fit regression model, increasing fasciculation frequency and median fasciculation amplitude were independently associated with the diagnosis of amyotrophic lateral sclerosis. Fasciculation frequency was the single best measure predictive of the disease, with an area under the curve of 0.89 (95% CI 0.81-0.98). The cut-off of more than 14 fasciculation potentials per minute achieved 80% sensitivity (95% CI 63%-90%) and 96% specificity (95% CI 78%- 100%). In conclusion, non-invasive measurement of fasciculation frequency at a single time-point reliably distinguished amyotrophic lateral sclerosis from its mimicking conditions and healthy individuals, warranting further research into its diagnostic applications.

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