Brain dynamics based automated epileptic seizure detection

Authors

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

Document Type

Article

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 (ASTLmax) 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.

Medical Subject Headings

Algorithms; Brain (physiopathology); Electroencephalography; Epilepsy (diagnosis, physiopathology); Humans; Scalp (physiopathology); Sensitivity and Specificity

Publication Date

1-1-2014

Publication Title

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

E-ISSN

2694-0604

Volume

2014

First Page

946

Last Page

9

PubMed ID

25570116

Digital Object Identifier (DOI)

10.1109/EMBC.2014.6943748

Recommended Citation

Venkataraman, V; Vlachos, I; Faith, A; Krishnan, B; Tsakalis, K; Treiman, D; and Iasemidis, L, "Brain dynamics based automated epileptic seizure detection" (2014). Translational Neuroscience. 1141.
https://scholar.barrowneuro.org/neurobiology/1141