Full-Length Original Research
Time-frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures
Article first published online: 3 MAY 2013
Wiley Periodicals, Inc. © 2013 International League Against Epilepsy
Volume 54, Issue 8, pages 1402–1408, August 2013
How to Cite
Bayly, J., Carino, J., Petrovski, S., Smit, M., Fernando, D. A., Vinton, A., Yan, B., Gubbi, J. R., Palaniswami, M. S. and O'Brien, T. J. (2013), Time-frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures. Epilepsia, 54: 1402–1408. doi: 10.1111/epi.12207
- Issue published online: 30 JUL 2013
- Article first published online: 3 MAY 2013
- Manuscript Accepted: 26 MAR 2013
- Psychogenic nonepileptic seizures;
- Epileptic seizures;
- Time-frequency mapping;
- Limb movements
A definite diagnosis of psychogenic nonepileptic seizures (PNES) usually requires in-patient video–electroencephalography (EEG) monitoring. Previous research has shown that convulsive psychogenic nonepileptic seizures (PNES) demonstrate a characteristic pattern of rhythmic movement artifact on the EEG. Herein we sought to examine the potential for time-frequency mapping of data from a movement-recording device (accelerometer) worn on the wrist as a diagnostic tool to differentiate between convulsive epileptic seizures and PNES.
Time-frequency mapping was performed on accelerometer traces obtained during 56 convulsive seizure-like events from 35 patients recorded during in-patient video-EEG monitoring. Twenty-six patients had PNES, eight had epileptic seizures, and one had both seizure types. The time-frequency maps were derived from fast Fourier transformations to determine the dominant frequency for sequential 2.56-s blocks for the course of each event.
The coefficient of variation (CoV) of limb movement frequency for the PNES events was less than for the epileptic seizure events (median, 17.18% vs. 52.23%; p < 0.001). A blinded review of the time-frequency maps by an epileptologist was accurate in differentiating between the event types, that is, 38 (92.7%) of 41 and 6 (75%) of 8 nonepileptic and epileptic seizures, respectively, were diagnosed correctly, with seven events classified as “nondiagnostic.” Using a CoV cutoff score of 32% resulted in similar classification accuracy, with 42 (93%) of 45 PNES and 10 (91%) of 11 epileptic seizure events correctly diagnosed.
Time-frequency analysis of data from a wristband movement monitor could be utilized as a diagnostic tool to differentiate between epileptic and nonepileptic convulsive seizure-like events.