EEG-EMG coherence estimated using time-varying autoregressive models in movement-activated myoclonus in patients with progressive myoclonic epilepsies.

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Abstract

We aimed this study at verifying the appropriateness of bivariate time-varying autoregressive models in detecting EEG-EMG relationships and identifying the characteristics of myoclonus-related EEG changes in patients with two forms of progressive myoclonus epilepsy (PME). Our results indicate that TVAR analysis was able to detect the presence of prominent peaks of EEG-EMG coherence between the EMG and contralateral frontocentral EEG derivation in all patients, revealing differences in time-frequency spectral profiles associated to the two different forms of PMEs, possibly correlated with the severity of myoclonus.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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