Description
Epilepsy affects numerous people around the world and is characterized by recurring seizures, prompting the ability to predict them so precautionary measures may be employed. One promising algorithm extracts spatiotemporal correlation based features from intracranial electroencephalography signals for use with support vector machines. The robustness of this methodology is tested through a sensitivity analysis. Doing so also provides insight about how to construct more effective feature vectors.
Details
Title
- Sensitivity Analysis of a Spatiotemporal Correlation Based Seizure Prediction Algorithm
Contributors
- Ma, Owen (Author)
- Bliss, Daniel (Thesis director)
- Berisha, Visar (Committee member)
- Barrett, The Honors College (Contributor)
- Electrical Engineering Program (Contributor)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2015-05
Resource Type
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