Epilepsy is a neurological disorder characterized by recurrent seizures. Approximately 30% of patients with epilepsy have seizures that are resistant to anti-epileptic drug (AED) therapy. If these patients are unable to undergo epilepsy surgery, they may choose to utilize the Vagus Nerve Stimulator (VNS) implant. The VNS therapy^® system has been approved by the FDA to electrically stimulate the left vagus nerve for epilepsy treatment. Patients with newly implanted VNS systems undergo an adjustment period of several months involving numerous medical check-ups to fine tune the electrical stimulation parameters based on clinical response. This sub-optimal adjustment method leaves the patient at risk of seizures and imposes financial burden. Identification of a marker of desired VNS operation would greatly expedite this adjustment process. The utility of non-invasive electroencephalogram (EEG), success of neural state classification research for diagnosis and treatment of neurological disorders, and the potential for real-time application due to advances of computer technology motivate this study. This dissertation outlines data mining approaches involving biclustering, logistic regression, and support vector machines as well as statistical comparisons of a range of relevant EEG dynamical measures for the characterization of electroencephalographic patterns associated with VNS therapy. The preliminary results are consistent with biological processes and clinical observations. One explanation for the electroencephalographic behavior is that VNS mimics a theorized therapeutic seizure effect where a seizure “resets” the brain from an unfavorable preictal state to a more favorable interictal state. The preliminary results suggest a connection between the EEG patterns and the stimulation parameters which may require a range of linear and nonlinear measures for adequate characterization.

In addition, support vector machines are utilized to create a seizure detection and stratification algorithm in patients with generalized absence epilepsy. The algorithm utilizes dynamic time warping distance and Teager-Kaiser energy as the representative EEG features. The algorithm performed slightly better at seizure onset detection than for seizure offset. This is likely due to increased waveform consistency shortly after onset compared to offset. Such an algorithm may benefit clinicians and researchers by providing a means to rapidly annotate EEG signals as well as a means to provide a clinically interesting measure of therapeutic efficacy for drug evaluation studies (e.g. distribution of seizure durations may vary before and after drug therapy, and thus a measure of this distribution may find clinically relevant information which a raw seizure count would miss). A future direction of this project is to test additional EEG feature inputs and assess how the algorithm copes with the challenges presented in online EEG analysis.