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    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.
    Bhattacharyya, Abhijit and Pachori, Ram Bilas 2017. A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform. IEEE Transactions on Biomedical Engineering, Vol. 64, Issue. 9, p. 2003.
    Javidan, Manouchehr 2012. Electroencephalography in Mesial Temporal Lobe Epilepsy: A Review. Epilepsy Research and Treatment, Vol. 2012, Issue. , p. 1.
    Zandi, Ali Shahidi Javidan, Manouchehr Dumont, Guy A and Tafreshi, Reza 2010. Automated Real-Time Epileptic Seizure Detection in Scalp EEG Recordings Using an Algorithm Based on Wavelet Packet Transform. IEEE Transactions on Biomedical Engineering, Vol. 57, Issue. 7, p. 1639.
    Zandi, Ali Shahidi Dumont, Guy A. Javidan, Manouchehr Tafreshi, Reza MacLeod, Bernard A. Ries, Craig R. and Puil, Ernie 2008. A novel wavelet-based index to detect epileptic seizures using scalp EEG signals. p. 919.
    Myles, S. Terence and Kaibara, Taro 2002. Epilepsy–Clinical Impact. Techniques in Neurosurgery, Vol. 7, Issue. 4, p. 332.
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Identification of the Temporal Components of Seizure Onset in the Scalp EEG

  • Nora S. O'Neill (a1), Zoltan J. Koles (a1) and Manouchehr Javidan (a2)
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.
RÉSUMÉ: Introduction:

L'identification, à l'ÉEG de surface, des signes les plus précoces d'oscillations rythmiques et d'événements paroxystiques associés à une crise épileptique est très importante pour la localisation du site d'origine de la crise. Dans cette étude, des filtres dépendants des données ont été conçus pour aider à mettre en évidence une activité masquée au début des crises dans les ÉEG problématiques.

 Méthodes:

Des filtres ont été élaborés en utilisant des motifs temporaux communs à des segments sélectionnés de portions pré-ictales et ictales d'ÉEGs de surface. Des motifs temporaux qui expliquaient une plus grande part de la variance dans le segment ictal que dans le segment pré-ictal de l'ÉEG de surface ont été utilisés pour élaborer les filtres.

 Résultats:

L'application des filtres à l'ÉEG de surface a mis en évidence des composantes temporales du début de la crise, qui n'étaient pas évidentes à l'enregistrement ÉEG non filtré. L'examen de l'ÉEG filtré a permis de reconnaître plus tôt le début des crises sur l'enregistrement. L'utilité des filtres a été confirmée qualitativement en comparant l'enregistrement de surface à l'enregistrement intracrânien et quantitativement en calculant les coefficients de corrélation entre les enregistrements de surface et intracrâniens avec et sans filtre.

 Conclusion:

L'approche à l'élaboration de filtres ÉEG selon les données permet la détection automatique des fréquences de base présentes au début des crises. Cette approche est plus efficace que le filtrage passe-bande étroit pour éliminer une interférence due à un artefactuelle ou autre qui peut masquer le début d'une crise. Ce type de filtre facilite l'identification du début des crises dans les enregistrements ÉEGs problématiques.

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COPYRIGHT: © The Canadian Journal of Neurological 2001
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Canadian Journal of Neurological Sciences
  • ISSN: 0317-1671
  • EISSN: 2057-0155
  • URL: /core/journals/canadian-journal-of-neurological-sciences
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