Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal Collapse

S.N. Kalitzin, P.R. Bauer, R.J. Lamberts, D.N. Velis, R.D. Thijs, F.H. Lopes Da Silva

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)

Abstract

Automated monitoring and alerting for adverse events in people with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently, we found a relation between clonic slowing at the end of a convulsive seizure (CS) and the occurrence and duration of a subsequent period of postictal generalized EEG suppression (PGES). Prolonged periods of PGES can be predicted by the amount of progressive increase of interclonic intervals (ICIs) during the seizure. The purpose of the present study is to develop an automated, remote video sensing-based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES. This may help preventing sudden unexpected death in epilepsy (SUDEP). The technique is based on our previously published optical flow video sequence processing paradigm that was applied for automated detection of major motor seizures. Here, we introduce an integral Radon-like transformation on the time-frequency wavelet spectrum to detect log-linear frequency changes during the seizure. We validate the automated detection and quantification of the ICI increase by comparison to the results from manually processed electroencephalography (EEG) traces as "gold standard". We studied 48 cases of convulsive seizures for which synchronized EEG-video recordings were available. In most cases, the spectral ridges obtained from Gabor-wavelet transformations of the optical flow group velocities were in close proximity to the ICI traces detected manually from EEG data during the seizure. The quantification of the slowing-down effect measured by the dominant angle in the Radon transformed spectrum was significantly correlated with the exponential ICI increase factors obtained from manual detection. If this effect is validated as a reliable precursor of PGES periods that lead to or increase the probability of SUDEP, the proposed method would provide an efficient alerting device.

Original languageEnglish
Article number1650027
Number of pages11
JournalInternational Journal of Neural Systems
Volume26
Issue number8
Early online date29 Jun 2016
DOIs
Publication statusPublished - Dec 2016

Keywords

  • Brain
  • Death, Sudden
  • Electroencephalography
  • Epilepsy
  • Humans
  • Image Interpretation, Computer-Assisted
  • Journal Article
  • Nonlinear Dynamics
  • Pattern Recognition, Automated
  • Radon transform
  • SUDEP
  • Seizures
  • Tertiary Care Centers
  • Validation Studies
  • Video Recording
  • Wavelet Analysis
  • clonic seizures
  • optic flow
  • video detection

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