Brain dynamics based automated epileptic seizure detection

Author

Venkataraman, V. ; Vlachos, I. ; Faith, A. ; Krishnan, B. ; Tsakalis, K. ; Treiman, D. ; Iasemidis, L.

Author_Institution

Sch. of Electr., Comput. & Energy Eng., Arizona State Univ., Tempe, AZ, USA

fYear

2014

fDate

26-30 Aug. 2014

Firstpage

946

Lastpage

949

Abstract

We developed and tested a seizure detection algorithm based on two measures of nonlinear and linear dynamics, that is, the adaptive short-term maximum Lyapunov exponent (ASTL_max) and the adaptive Teager energy (ATE). The algorithm was tested on long-term (0.5-11.7 days) continuous EEG recordings from five patients (3 with intracranial and 2 with scalp EEG) with a total of 56 seizures, producing a mean sensitivity of 91% and mean specificity of 0.14 false positives per hour. The developed seizure detection algorithm is data-adaptive, training-free, and patient-independent.

Keywords

electroencephalography; medical disorders; medical signal detection; EEG recordings; adaptive Teager energy; adaptive short-term maximum Lyapunov exponent; brain dynamics based automated epileptic seizure detection; data-adaptive training-free patient-independent seizure detection algorithm; intracranial EEG; linear dynamics; nonlinear dynamics; scalp EEG; Detection algorithms; Electrodes; Electroencephalography; Epilepsy; Scalp; Sensitivity; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE

Conference_Location

Chicago, IL

ISSN

1557-170X

Type

conf

DOI

10.1109/EMBC.2014.6943748

Filename

6943748