Quantitative analysis of surface electromyography signals during epileptic and non-epileptic seizures

Sandor Beniczky(1, 2), Isa Conradsen(1, 3), Mihai Moldovan(4, 5), Poul Jennum(6), Martin Fabricius(7), Krisztina Benedek(7), Noemi Andersen(8), Peter Wolf(9) 1 Danish Epilepsy Centre, Department of Clinical Neurophysiology, Dianalund, Denmark 2 Aarhus University Hospital, Department of Clinical Neurophysiology, Aarhus C, Denmark 3 IctalCare A/S, Hørsholm, Denmark 4 Neuroscience and Pharmacology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark 5 Department of Physiology, University of Medicine and Pharmacy Carol Davila, Bucharest, Romania; 6 Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Faculty of Health Sciences, University of Copenhagen, Glostrup Hospital, Glostrup, Denmark 7 Department of Clinical Neurophysiology, University of Copenhagen, Glostrup Hospital, Glostrup, Denmark 7 Department of Clinical Neurophysiology, University of Copenhagen, Glostrup Hospital, Glostrup, Denmark 8 Department of Neurology, University of Copenhagen, Glostrup Hospital, Glostrup, Denmark 9 Danish Epilepsy Centre, Department of Neurology, Dianalund, Denmark<

Published in:
Epilepsia. 2014 Jul;55(7):1128-34. doi: 10.1111/epi.12669. Epub 2014 Jun 2.


To investigate the characteristics of sustained muscle activation during convulsive epileptic and psychogenic nonepileptic seizures (PNES), as compared to voluntary muscle activation. The main goal was to find surface electromyography (EMG) features that can distinguish between convulsive epileptic seizures and convulsive PNES.

In this case-control study, surface EMG was recorded from the deltoid muscles during long-term video-electroencephalography (EEG) monitoring in 25 patients and in 21 healthy controls. A total of 46 clinical episodes were recorded: 28 generalized tonic-clonic seizures (GTCS) from 14 patients with epilepsy, and 18 convulsive PNES from 12 patients (one patient had both GTCS and PNES). The healthy controls were simulating GTCS. To quantitatively characterize the signals we calculated the following parameters: root mean square (RMS) of the amplitude, median frequency (MF), coherence, and duration of the seizures, of the clonic EMG discharges, and of the silent periods between the cloni. Based on wavelet analysis, we distinguished between a low-frequency component (LF 2-8 Hz) and a high-frequency component (HF 64-256 Hz).

Duration of the seizure, and separation between the tonic and the clonic phases distinguished at group-level but not at individual level between convulsive PNES and GTCS. RMS, temporal dynamics of the HF/LF ratio, and the evolution of the silent periods differentiated between epileptic and nonepileptic convulsive seizures at the individual level. A combination between HF/LF ratio and RMS separated all PNES from the GTCS. A blinded review of the EMG features distinguished correctly between GTCS and convulsive PNES in all cases. The HF/LF ratio and the RMS of the PNES were smaller compared to the simulated seizures.

In addition to providing insight into the mechanism of muscle activation during convulsive PNES, these results have diagnostic significance, at the individual level. Surface EMG features can accurately distinguish convulsive epileptic from nonepileptic psychogenic seizures, even in PNES cases without rhythmic clonic movements.

Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.