Print Email Facebook Twitter Investigation of focal epilepsy using graph signal processing Title Investigation of focal epilepsy using graph signal processing Author Zin, Gaia (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Isufi, E. (mentor) Hunyadi, Borbala (mentor) Degree granting institution Delft University of Technology Programme Electrical Engineering | Signals and Systems Date 2021-11-24 Abstract Epilepsy is one of the most common neurological disorders worldwide. Its manifestations, the seizures, are due to a group of neurons' abnormal and synchronous activity. The unpredictable nature of these events hinders the quality of life of those affected. In particular, focal seizures show a localized onset of the abnormal activity and are the most common ones. Correct detection of the episodes can help clinicians to give the best medical treatments. This research project arises from the need to have automatic algorithms for seizure detection with a high number of correctly detected seizures for low false alarm rates. Recent studies have shown disorganization in how brain areas interact with each other before and during a seizure. We decided to model this change in connectivity patterns by inferring graphs from EEG recordings of epileptic patients. We work with seventeen subjects suffering from focal epilepsy, and we build, for each of them, a graph of the activity preceding (preictal) and during (ictal) a seizure. After that, we exploit techniques from graph signal processing to build a detector for seizures. Last, we analyze the density of connections of the inferred graphs to indicate the seizure onsets. The obtained results are unsuitable for real-life applications, but they are a starting point for further research. Furthermore, we find that most the proposed ictal or preictal graphs show less connections in the nodes involved with the seizure onset. Subject focal epilepsygraphGraph Fourier TransformDetection To reference this document use: http://resolver.tudelft.nl/uuid:ad377ff6-9979-4a29-9ab9-03296fc87317 Part of collection Student theses Document type master thesis Rights © 2021 Gaia Zin Files PDF MasterThesis_GaiaZin5118689.pdf 14.21 MB Close viewer /islandora/object/uuid:ad377ff6-9979-4a29-9ab9-03296fc87317/datastream/OBJ/view