Epileptic seizure detection from ECoG using recurrence time statistics

Author

Liu, Hui ; Gao, J.B. ; Hild, Kenneth E., II ; Príncipe, José C. ; Sackellares, J. Chris

Author_Institution

Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL, USA

Volume

1

fYear

2004

fDate

1-5 Sept. 2004

Firstpage

29

Lastpage

32

Abstract

A recurrence time statistics T1 is defined and used as a feature extraction method for seizure detection. The preliminary data shows that during seizure T1 generates a peak and this peak clearly distinguishes the seizure state from background activity. When applied to multi-channel ECoG recordings, the spatial-temporal signature of T1 can be clearly observed to discriminate seizures. The T1 feature was used for automated seizure detection on two sets of long term monitoring ECoG data. The detection probability reached 97% with a 0.29 per hour average false alarm rate.

Keywords

electroencephalography; feature extraction; medical signal detection; medical signal processing; electrocorticography; epileptic seizure detection; feature extraction; recurrence time statistics; Biomedical measurements; Brain modeling; Character generation; Electroencephalography; Entropy; Epilepsy; Feature extraction; Signal analysis; Signal generators; Statistics; electrocorticography (ECoG); epilepsy; receiver operating curve; recurrence time statistics; seizure detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE

Conference_Location

San Francisco, CA

Print_ISBN

0-7803-8439-3

Type

conf

DOI

10.1109/IEMBS.2004.1403082

Filename

1403082