N S O'Neill1, M Javidan, Z J Koles. 1. Department of Biomedical Engineering, University of Alberta, Edmonton, Canada.
Abstract
BACKGROUND: The identification of the earliest indication of rhythmical oscillations and paroxysmal events associated with an epileptic seizure is paramount in identifying the location of the seizure onset in the scalp EEG. In this work, data-dependent filters are designed that can help reveal obscure activity at the onset of seizures in problematic EEGs. METHODS: Data-dependent filters were designed using temporal patterns common to selected segments from pre-ictal and ictal portions of the scalp EEG. Temporal patterns that accounted for more variance in the ictal segment than in the pre-ictal segment of the scalp EEG were used to form the filters. RESULTS: Application of the filters to the scalp EEG revealed temporal components in the seizure onset in the scalp recording that were not obvious in the unfiltered EEG. Examination of the filtered EEG enabled the onset of the seizure to be recognized earlier in the recording. The utility of the filters was confirmed qualitatively by comparing the scalp recording to the intracranial recording and quantitatively by calculating correlation coefficients between the scalp and intracranial recordings before and after filtering. CONCLUSION: The data-dependent approach to EEG filter design allows automatic detection of the basic frequencies present in the seizure onset. This approach is more effective than narrow band-pass filtering for eliminating artifactual and other interference that can obscure the onset of a seizure. Therefore, temporal-pattern filtering facilitates the identification of seizure onsets in challenging scalp EEGs.
BACKGROUND: The identification of the earliest indication of rhythmical oscillations and paroxysmal events associated with an epilepticseizure is paramount in identifying the location of the seizure onset in the scalp EEG. In this work, data-dependent filters are designed that can help reveal obscure activity at the onset of seizures in problematic EEGs. METHODS: Data-dependent filters were designed using temporal patterns common to selected segments from pre-ictal and ictal portions of the scalp EEG. Temporal patterns that accounted for more variance in the ictal segment than in the pre-ictal segment of the scalp EEG were used to form the filters. RESULTS: Application of the filters to the scalp EEG revealed temporal components in the seizure onset in the scalp recording that were not obvious in the unfiltered EEG. Examination of the filtered EEG enabled the onset of the seizure to be recognized earlier in the recording. The utility of the filters was confirmed qualitatively by comparing the scalp recording to the intracranial recording and quantitatively by calculating correlation coefficients between the scalp and intracranial recordings before and after filtering. CONCLUSION: The data-dependent approach to EEG filter design allows automatic detection of the basic frequencies present in the seizure onset. This approach is more effective than narrow band-pass filtering for eliminating artifactual and other interference that can obscure the onset of a seizure. Therefore, temporal-pattern filtering facilitates the identification of seizure onsets in challenging scalp EEGs.