Epilepsy affects approximately 1% of the population and can be as benign as causing social awkwardness or as malign as causing death. Using algorithms to predict seizures can lead to event and danger mitigation that could treat a subset of epileptics. The prediction algorithm uses Takens' theorem to generate phase space graphs that can be used to discover EEG anomalies, which would lead to the pre-ictal state. The phase space graphs that patients' EEG generate are compared to stored phase space graphs in order to observe a phase transition. Support Vector Machines are able to achieve high accuracy as the machine learning method chosen to learn the relevant patterns and make the predictions based on observed anomalies.
|Advisor:||McDonald, Jeffrey T.|
|Commitee:||Hively, Lee M., Johnsten, Tom D., McDonald, Jeffrey T.|
|School:||University of South Alabama|
|School Location:||United States -- Alabama|
|Source:||MAI 55/02M(E), Masters Abstracts International|
|Subjects:||Neurosciences, Artificial intelligence, Computer science|
|Keywords:||Anomaly, Chaos, Epilepsy, Phase-transition, Prediction, Svm|
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