2010 · 29 citations · 11 references
ComputingComputer ScienceNeurologyEpilepsyMachine LearningTime Series AnalysisElectroencephalogram AnalysisElectroencephalogram Signal ProcessingElectroencephalographyElectroencephalography In EpilepsySignal Detection TheorySupport Vector MachinesAutomated Seizure Detection AlgorithmsEeg Feature ExtractionEeg-based Seizure DetectionElectroencephalography Feature ExtractionEpileptic Seizure DetectionIntracranial ElectroencephalographySeizure DetectionSupport Vector Machine Classification
An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose. The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before, making this a pilot study. Data from red different patients with 6 to 49 seizures are used to test our model. Three patients are recorded with scalp electroencephalography (sEEG) and three with intracranial electroencephalography (iEEG). A sensitivity of 78-100% and a detection latency of 5-18s has been achieved, while holding the false detection at 0.16-5.31/h. Our results show the potential of Matching Pursuit as a feature extractor for detection of epileptic seizures.
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