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Objective: There is accumulated evidence that mesial temporallobe seizures are preceded by a preictal transition that evolves over minutes to hours. In the pr esent study, we investigated these possible preictal changes in long-term intra cranial recordings of five patients by a measure of phase synchronization.In ord er to clearly distinguish preictal changes from all the other interictal states, we developed an automatic extraction of representative patterns of interictal s ynchronization activity.This reference library was used to classify the successi ve synchronization patterns of long-term recordings into groups of similar patt erns. Altered states of brain synchronization were identified as deviating from patterns in the reference library and were evaluated relative to the times of se izure onset in terms of sensitivity and specificity. Methods: A phase-locking m easure was estimated using a sliding window analysis on 15 frequency bands (2 Hz steps between 0 and 30 Hz), for all pairs of EEG channels in the epileptogenic temporal lobe (14-20 channels), over the entire data sets (total analyzed durat ion 305 h). The preictal identification encompasses three basic stages: (1) a pr eprocessing stage involving the determination of a reference library of characte ristic interictal synchronization patterns using a K-means algorithm, and the identification of discriminant variables different iating interictal from preictal states, (2) a classification stage of the synchr onization pattern via a minimum Mahalanobis distance to the reference patterns,a s well as detection of outliers, (3) an evaluation stage of the sensitivity and specificity of the detection by receiver-operating characteristic curves. Resul ts: In most of the cases (36 of 52 seizures, i.e. 70%), a specific state of bra in synchronization can be observed several hours before the actual seizure. The changes involved both increases and decreases of the synchronization levels, occ urring mostly within the 4-15 Hz frequency band, and were often localized near the primary epileptogenic zone. Conclusions: The analysis of phase synchronizati on offers a way to distinguish between a preictal state and normal interictal ac tivity. These findings suggest that brain synchronizations are preictally altere d in the epileptogenic temporal lobe, inducing a pathological state of higher su sceptibility for seizure activity. Significance: Phase synchronization is capabl e of extracting information from the EEG that allow the definition of a preictal state. Although the proposed analysis does not constitute genuine seizure antic ipation, these changes in neuronal synchronization may provide helpful informati on for prospective seizure warning.
Objective: There is accumulated evidence that mesial temporallobe seizures are preceded by a preictal transition that evolves over minutes to hours. In the pr esent study, we investigated these possible preictal changes in long-term intra cranial recordings of five patients by a measure of phase synchronization.In ord er to clearly distinguish preictal changes from all the other interictal states, we developed an automatic extraction of representative patterns of interictal s ynchronization activity. this reference library was used to classify the successi ve synchronization patterns of long-term recordings into groups of similar patt erns. 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 m easure was estimated using a sliding window analysis on 15 frequency bands (2 Hz steps between 0 and 30 Hz), for all pairs of EEG channels in the epileptogenic temporal lobe (14-20 channels), over the entire data sets (total analyzed delay 305 h). The preictal identification encompasses three basic stages: (1) a pr eprocessing stage involving the determination of a reference library of characte ristic interictal synchronization patterns using a K-means algorithm, and the identification of discriminant variables different iating interictal from preictal states, (2) a classification stage of the synchrization 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. Resul ts: In most of the cases (36 of 52 seizures, ie 70%), a specific state of bra in synchronization can be observed for hours before the actual seizure. The changes involved both increases and decreases of the synchronization levels, occ urring mostly within the 4-15 Hz frequency band, and were often localized near the primary epileptogenic zone. Conclusions: The analysis of phase synchronizati on offers a way to distinguish between a preictal state and normal interictal ac tivity. These findings suggest that brain synchronizations are preictally altere d: the epileptogenic temporal lobe, inducing a pathological state of higher su sceptibility for seizure activity. Significance: Phase synchronization is capabl e of extracting information from the EEG that allow the definition of a preictal state. antic ipation, these changes in neuronal synchronization may provide helpful informati on for prospective seizure warning.