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Extracting a seizure intensity index from one-channel EEG signal using detrended fluctuation analysis and bispectral analysis

Extracting a seizure intensity index from one-channel EEG signal using detrended fluctuation analysis and bispectral analysis
Extracting a seizure intensity index from one-channel EEG signal using detrended fluctuation analysis and bispectral analysis
Epilepsy is a medical condition that produces seizures affecting a variety of mental and physical functions. Seizures can last from a few seconds to a few minutes. They can have many symptoms, from convulsions and loss of consciousness to blank staring, lip smacking, or jerking movements of arms and legs. If early warning signals of an upcoming seizure (diagnosis of preictal period) are detected, proper treatment can be applied to the patient to help prevent the seizure. In this research, an epileptic disorder has been divided into three subsets: Normal, Preictal (just before the seizure), and Ictal (during seizure). By using Detrended Fluctuation Analysis (DFA), Bispectral Analysis (BIS), and Standard Deviation (SD) three features from single-channel EEG signals have been derived in the foresaid groups. A fuzzy classifier is used to separate the three groups which can successfully separate them with a separation degree of 100% and further a fuzzy inference engine is used to extract a Seizure Intensity Index (SII) from the Electroencephalogram (EEG) signals of the three different states. One can apparently see the distinction of SII amounts between the three states. It is more important when one remembers that these results are just from single-channel EEG signal.
epilepsy, fuzzy inference engine, bispectrum, detrended fluctuation analysis
1937-6871
253-261
Hosseini, P.T.
47511a4b-5adc-4e93-9d2a-46e3016c87fb
Shalbaf, Reza
5a69daee-50e7-4222-ae82-044365c73b53
Nasrabadi, Ali Motie
17730fa6-dff6-4580-967a-8523c62df063
Hosseini, P.T.
47511a4b-5adc-4e93-9d2a-46e3016c87fb
Shalbaf, Reza
5a69daee-50e7-4222-ae82-044365c73b53
Nasrabadi, Ali Motie
17730fa6-dff6-4580-967a-8523c62df063

Hosseini, P.T., Shalbaf, Reza and Nasrabadi, Ali Motie (2010) Extracting a seizure intensity index from one-channel EEG signal using detrended fluctuation analysis and bispectral analysis. Journal of Biomedical Science and Engineering, 3 (3), 253-261.

Record type: Article

Abstract

Epilepsy is a medical condition that produces seizures affecting a variety of mental and physical functions. Seizures can last from a few seconds to a few minutes. They can have many symptoms, from convulsions and loss of consciousness to blank staring, lip smacking, or jerking movements of arms and legs. If early warning signals of an upcoming seizure (diagnosis of preictal period) are detected, proper treatment can be applied to the patient to help prevent the seizure. In this research, an epileptic disorder has been divided into three subsets: Normal, Preictal (just before the seizure), and Ictal (during seizure). By using Detrended Fluctuation Analysis (DFA), Bispectral Analysis (BIS), and Standard Deviation (SD) three features from single-channel EEG signals have been derived in the foresaid groups. A fuzzy classifier is used to separate the three groups which can successfully separate them with a separation degree of 100% and further a fuzzy inference engine is used to extract a Seizure Intensity Index (SII) from the Electroencephalogram (EEG) signals of the three different states. One can apparently see the distinction of SII amounts between the three states. It is more important when one remembers that these results are just from single-channel EEG signal.

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More information

Published date: March 2010
Keywords: epilepsy, fuzzy inference engine, bispectrum, detrended fluctuation analysis

Identifiers

Local EPrints ID: 192047
URI: https://eprints.soton.ac.uk/id/eprint/192047
ISSN: 1937-6871
PURE UUID: 12d19462-8916-48ea-8d90-a7e25d7705ec

Catalogue record

Date deposited: 29 Jun 2011 11:02
Last modified: 18 Jul 2017 11:32

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Contributors

Author: P.T. Hosseini
Author: Reza Shalbaf
Author: Ali Motie Nasrabadi

University divisions

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