Phase synchronization with ICA for epileptic seizure onset prediction in the long term EEG.

Gupta, Divya, James, Caroline Jane and Gray, William Peter ORCID: https://orcid.org/0000-0001-7595-8887 2008. Phase synchronization with ICA for epileptic seizure onset prediction in the long term EEG. Presented at: 4th International Conference on Advances in Medical Signal and Information Processing, Santa Margherita Ligure, Italy), 14-16 July 2008. Advances in Medical, Signal and Information Processing, 2008, MEDSIP 2008, 4th IET International Conference. IET Conference Publications , vol.2008 (CP540) Piscataway, NJ: IEEE, 10.1049/cp:20080427

Full text not available from this repository.

Abstract

The apparently unpredictable nature of epileptic seizures can be devastating for people with epilepsy. Current medical interventions can help 75% of patients while 25% have to live with uncontrolled seizures. This motivates the search for a seizure prediction prototype using electroencephalograms (electrical signals that capture brain activity). The concept of phase synchrony has attracted much attention recently in the context of seizure prediction but is still in need of further study. The basis of our analysis is to track changes in synchrony in brain signals at and before seizure onset. The novel concept in our analysis is the use of unmixed signals as opposed to scalp EEG signals for phase synchrony analysis. The unmixing is performed by a Blind Source Separation technique called Independent component Analysis (ICA). ICA seeks underlying independent source signals from the EEG and it allows multivariate analysis using spatial as well as temporal information inherent to EEG signals. The present study on long-term continuous EEG data sets indicates that the concept of using phase synchronization with ICA may prove useful for predicting seizures.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Biosciences
Subjects:	Q Science > Q Science (General) R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords:	Independent component analysis; phase synchronisation; EEG; epilepsy; seizure prediction
Publisher:	IEEE
ISBN:	9780863419348
Last Modified:	10 Feb 2024 02:07
URI:	https://orca.cardiff.ac.uk/id/eprint/24039

Citation Data

Cited 3 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item