Analyzing Predictive Features of Epileptic Seizures in Human Intracranial EEG Recordings
Name
Mari Liis Velner
Abstract
Epilepsy seizure prediction is a challenge that scientists have tried to overcome throughout many decades, using different state-of-the-art features and machine learning methods. If a forecasting system could predict and warn epilepsy patients of impeding seizures in real time, it would greatly improve their quality of life. Seizure prediction consists of two stages: feature extraction from the data and sample classification to interictal (non-seizure) or preictal (preseizure) state. EEG data is commonly used, as it is inexpensive, portable and it most clearly reflects the changes in the brain’s dynamics. While most studies focus on extracting novel features or using new classifiers, this Thesis focuses on ascertaining the most significant features among some that are commonly used in seizure prediction. Knowing which features influence the prediction results the most, helps to understand the inner workings of both the classifiers and the brain activity and to reduce the feature set in future research, making the classification process more effective. Intracranial EEG data of two patients was used in this Thesis with three classifiers from the scikit-learn library, which were combined with methods for evaluating feature importance. Moderately good to excellent prediction accuracies were achieved with these methods, which allowed to reliably analyze the feature importance results of the different classifiers.
Graduation Thesis language
English
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Raul Vicente Zafra
Defence year
2017