Processing of Epileptic EEG

Gath, Isak; Harris, Bernard; Salant, Y.; Feuerstein, Claude; Henriksen, Olaf; Rondouin, G.

doi:10.1007/978-1-4757-9098-6_5

Cited by 1 publication

(2 citation statements)

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“…A host of signal processing methods have been put forward for application to pre-or interictal EEG, both for classification following parameter extraction and for supervised pattern recognition [2], [4]- [21]. Several specific attempts at epilepsy prediction have been made, both on experimental animals and human epileptic patients [22]- [29]. Of those based on sharp-transient detection, the few successful ones lacked universality, being reliant on prior familiarity with the individual's seizure discharge patterns [23], [26].…”

Section: Introductionmentioning

confidence: 99%

“…Of those based on sharp-transient detection, the few successful ones lacked universality, being reliant on prior familiarity with the individual's seizure discharge patterns [23], [26]. Some attempts, which tracked nonspecific changes in background activity, found them too close (a few seconds) to the seizure to be practical [24], [27]- [29].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering

Geva

Kerem

1998

IEEE Trans. Biomed. Eng.

113

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Abstract-Dynamic state recognition and event-prediction are fundamental tasks in biomedical signal processing. We present a new, electroencephalogram (EEG)-based, brain-state identification method which could form the basis for forecasting a generalized epileptic seizure. The method relies on the existence in the EEG of a preseizure state, with extractable unique features, a priori undefined.We exposed 25 rats to hyperbaric oxygen until the appearance of a generalized EEG seizure. EEG segments from the preexposure, early exposure, and the period up to and including the seizure were processed by the fast wavelet transform. Features extracted from the wavelet coefficients were inputted to the unsupervised optimal fuzzy clustering (UOFC) algorithm.The UOFC is useful for classifying similar discontinuous temporal patterns in the semistationary EEG to a set of clusters which may represent brain-states. The unsupervised selection of the number of clusters overcomes the a priori unknown and variable number of states. The usually vague brain state transitions are naturally treated by assigning each temporal pattern to one or more fuzzy clusters.The classification succeeded in identifying several, behaviorbacked, EEG states such as sleep, resting, alert and active wakefulness, as well as the seizure. In 16 instances a preseizure state, lasting between 0.7 and 4 min was defined. Considerable individual variability in the number and characteristics of the clusters may postpone the realization of an early universal epilepsy warning. Universality may not be crucial if using a dynamic version of the UOFC which has been taught the individual's normal vocabulary of EEG states and can be expected to detect unspecified new states.

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