Electroencephalographic events prior to epileptic major motor seizures
Petkov, G; Kalitzin, S; Velis, D; et al.Vledder, B; Koppert, M; Lopes da Silva, F
Date: 10 November 2012
Conference paper
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
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Abstract
Rationale. The goal of this study is to evaluate the
electroencephalographic (EEG) events, prior to clonic phases of
epileptic motor seizures. Analyzing video sequences we were
able to detect these special phases of motor seizures, by image
features. This can be used for an early detection and alerting
for these events. In the ...
Rationale. The goal of this study is to evaluate the
electroencephalographic (EEG) events, prior to clonic phases of
epileptic motor seizures. Analyzing video sequences we were
able to detect these special phases of motor seizures, by image
features. This can be used for an early detection and alerting
for these events. In the study we analyzed 42 seizures. Based on
collected data we compare the quantitative results from video
detection of seizures with the features computed from EEG
scalp recordings from about 3 minutes prior to the seizure. We
analyze the non-stationary frequency spectrum of the EEG
recordings and match it against our automated video detection
output in order to investigate possible precursory EEG events.
Methods. Video recordings are analyzed by applying optical
flow theory, reconstruction of geometrical flow invariants, low
and high pass filtering, and suitable normalizations. EEG
recordings are processed with use of a Gabor wavelet
technique. Comparison is achieved by means of analysis of the
cross-correlation function between the derivatives of the Gabor
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video detection algorithm. Results. In the present study certain
ranges of EEG frequencies were found, where electro-graphical
events precede clonic phases of clinical motor seizures from 2-8
up to 30-40 seconds. These results could be used for
construction of new generation of methods for automated
motor seizure detection.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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