Epileptic seizure prediction by extracting relative and fine changes of signal transitions

Mohammad Parvez, Manoranjan Paul

Research output: Book chapter/Published conference paperConference paper

Abstract

Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark dataset in different brain locations compared to the existing state-of-the-art methods.
Original languageEnglish
Title of host publicationEMBC 2015
Place of PublicationUSA
PublisherIEEE
Pages1-6
Number of pages6
Publication statusPublished - 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Milano Conference Center , Milan, Italy
Duration: 25 Aug 201529 Aug 2015
https://embc.embs.org/2015/

Conference

Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
CountryItaly
CityMilan
Period25/08/1529/08/15
Internet address

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