OBJECTIVE: There is accumulated evidence that mesial temporal lobe seizures are preceded by a preictal transition that evolves over minutes to hours. In the present study, we investigated these possible preictal changes in long-term intracranial recordings of five patients by a measure of phase synchronization. In order to clearly distinguish preictal changes from all the other interictal states, we developed an automatic extraction of representative patterns of interictal synchronization activity. This reference library was used to classify the successive synchronization patterns of long-term recordings into groups of similar patterns. Altered states of brain synchronization were identified as deviating from patterns in the reference library and were evaluated relative to the times of seizure onset in terms of sensitivity and specificity. METHODS: A phase-locking measure was estimated using a sliding window analysis on 15 frequency bands (2Hz steps between 0 and 30Hz), for all pairs of EEG channels in the epileptogenic temporal lobe (14-20 channels), over the entire data sets (total analyzed duration 305h). The preictal identification encompasses three basic stages: (1) a preprocessing stage involving the determination of a reference library of characteristic interictal synchronization patterns using a K-means algorithm, and the identification of discriminant variables differentiating interictal from preictal states, (2) a classification stage of the synchronization pattern via a minimum Mahalanobis distance to the reference patterns, as well as detection of outliers, (3) an evaluation stage of the sensitivity and specificity of the detection by receiver-operating characteristic curves. RESULTS: In most of the cases (36 of 52 seizures, i.e. 70%), a specific state of brain synchronization can be observed several hours before the actual seizure. The changes involved both increases and decreases of the synchronization levels, occurring mostly within the 4-15Hz frequency band, and were often localized near the primary epileptogenic zone. CONCLUSIONS: The analysis of phase synchronization offers a way to distinguish between a preictal state and normal interictal activity. These findings suggest that brain synchronizations are preictally altered in the epileptogenic temporal lobe, inducing a pathological state of higher susceptibility for seizure activity. SIGNIFICANCE: Phase synchronization is capable of extracting information from the EEG that allow the definition of a preictal state. Although the proposed analysis does not constitute genuine seizure anticipation, these changes in neuronal synchronization may provide helpful information for prospective seizure warning.
OBJECTIVE: There is accumulated evidence that mesial temporal lobe seizures are preceded by a preictal transition that evolves over minutes to hours. In the present study, we investigated these possible preictal changes in long-term intracranial recordings of five patients by a measure of phase synchronization. In order to clearly distinguish preictal changes from all the other interictal states, we developed an automatic extraction of representative patterns of interictal synchronization activity. This reference library was used to classify the successive synchronization patterns of long-term recordings into groups of similar patterns. Altered states of brain synchronization were identified as deviating from patterns in the reference library and were evaluated relative to the times of seizure onset in terms of sensitivity and specificity. METHODS: A phase-locking measure was estimated using a sliding window analysis on 15 frequency bands (2Hz steps between 0 and 30Hz), for all pairs of EEG channels in the epileptogenic temporal lobe (14-20 channels), over the entire data sets (total analyzed duration 305h). The preictal identification encompasses three basic stages: (1) a preprocessing stage involving the determination of a reference library of characteristic interictal synchronization patterns using a K-means algorithm, and the identification of discriminant variables differentiating interictal from preictal states, (2) a classification stage of the synchronization pattern via a minimum Mahalanobis distance to the reference patterns, as well as detection of outliers, (3) an evaluation stage of the sensitivity and specificity of the detection by receiver-operating characteristic curves. RESULTS: In most of the cases (36 of 52 seizures, i.e. 70%), a specific state of brain synchronization can be observed several hours before the actual seizure. The changes involved both increases and decreases of the synchronization levels, occurring mostly within the 4-15Hz frequency band, and were often localized near the primary epileptogenic zone. CONCLUSIONS: The analysis of phase synchronization offers a way to distinguish between a preictal state and normal interictal activity. These findings suggest that brain synchronizations are preictally altered in the epileptogenic temporal lobe, inducing a pathological state of higher susceptibility for seizure activity. SIGNIFICANCE: Phase synchronization is capable of extracting information from the EEG that allow the definition of a preictal state. Although the proposed analysis does not constitute genuine seizure anticipation, these changes in neuronal synchronization may provide helpful information for prospective seizure warning.
Authors: C A Schevon; J Cappell; R Emerson; J Isler; P Grieve; R Goodman; G McKhann; H Weiner; W Doyle; R Kuzniecky; O Devinsky; F Gilliam Journal: Neuroimage Date: 2007-01-16 Impact factor: 6.556
Authors: Edwin van Dellen; Linda Douw; Johannes C Baayen; Jan J Heimans; Sophie C Ponten; W Peter Vandertop; Demetrios N Velis; Cornelis J Stam; Jaap C Reijneveld Journal: PLoS One Date: 2009-11-26 Impact factor: 3.240