Patient un-specific detection of epileptic seizures through changes in variance.
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.
Varsavsky
Andrea
A
Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic. a.varsavsky@ee.unimelb.edu.au
Mareels
Iven
I
D000465:Algorithms; D001364:Awareness; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000503:physiopathology; D004562:Electrocardiography; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D016014:Linear Models; D008959:Models, Neurological; D008991:Monitoring, Physiologic; D012815:Signal Processing, Computer-Assisted