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Updated: Jul 10, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
Published on: December 18, 2016
Patient un-specific detection of epileptic seizures through changes in variance
Andrea Varsavsky1, Iven Mareels
1Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic. a.varsavsky@ee.unimelb.edu.au
Abstract:
Despite much progress and research, fully reliable computer based epileptic seizure detection in EEG recordings is still elusive. This paper outlines a new strategy toward seizure detection. It is proposed that it is not the precise nature of a statistic that is important, but rather its variance over time. Using this, algorithms are presented that are able to successfully identify 97.6% of seizures from over 170 hours of recording and 15 different patients. False positives remain high, but virtually no pre-processing has been applied to the raw data and it is expected that this can be improved with further work.
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