Approximately one percent of the world's population exhibits symptoms of epilepsy, a serious disorder of the central nervous system that predisposes those affected to experiencing recurrent seizures. The risk of injury associated with epileptic seizures might be mitigated by the use of a device that can reliably detect or predict the onset of seizure episodes and then warn caregivers of the event. In a hospital this device could also be used to initiate time-sensitive clinical procedures necessary for characterizing epileptic syndromes. This thesis discusses the design of a real-time, patient-specific method that can be used to detect the onset of epileptic seizures in non-invasive EEG, and then initiate time-sensitive clinical procedures like ictal SPECT. We adopt a patient-specific approach because of the clinically observed consistency of seizure and non-seizure EEG characteristics within patients, and their great heterogeneity across patients. We also treat patient-specific seizure onset detection as a binary classification problem. Our observation is a multi-channel EEG signal; its features include amplitude, fundamental frequency, morphology, and spatial localization on the scalp; and it is classified as an instance of non-seizure or seizure EEG based on the learned features of training examples from a single patient. We use a multi-level wavelet decomposition to extract features that capture the amplitude, fundamental frequency, and morphology of EEG waveforms. These features are then classified using a support vector machine or maximum-likelihood classifier trained on a patient's seizure and non-seizure EEG; non-seizure EEG includes normal and artifact contaminated EEG from various states of consciousness.
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