A Functional-Genetic Scheme for Seizure Forecasting in Canine Epilepsy

Objective: The objective of this work is the development of an accurate seizure forecasting algorithm that considers brain's functional connectivity for electrode selection. Methods: We start by proposing Kmeans-directed transfer function, an adaptive functional connectivity method intended for...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2018-06, Vol.65 (6), p.1339-1348
Main Authors: Bou Assi, Elie, Nguyen, Dang K., Rihana, Sandy, Sawan, Mohamad
Format: Article
Language:English
Subjects:
Algorithms
Animals
Brain
Classification
Cluster Analysis
Convulsions & seizures
directed transfer function
Dogs
EEG
Electrocorticography - methods
Electrodes
Electroencephalography
Epilepsy
Epilepsy - diagnosis
Epilepsy - physiopathology
Feasibility studies
Feature extraction
Forecasting
functional connectivity
genetic algorithm
Genetic algorithms
Hemispheres
Implantation
Localization
Neural networks
Prediction algorithms
Seizing
seizure forecasting
Seizures
Signal Processing, Computer-Assisted
Support Vector Machine
Support vector machines
Transfer functions
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Summary:Objective: The objective of this work is the development of an accurate seizure forecasting algorithm that considers brain's functional connectivity for electrode selection. Methods: We start by proposing Kmeans-directed transfer function, an adaptive functional connectivity method intended for seizure onset zone localization in bilateral intracranial EEG recordings. Electrodes identified as seizure activity sources and sinks are then used to implement a seizure-forecasting algorithm on long-term continuous recordings in dogs with naturally-occurring epilepsy. A precision-recall genetic algorithm is proposed for feature selection in line with a probabilistic support vector machine classifier. Results: Epileptic activity generators were focal in all dogs confirming the diagnosis of focal epilepsy in these animals while sinks spanned both hemispheres in 2 of 3 dogs. Seizure forecasting results show performance improvement compared to previous studies, achieving average sensitivity of 84.82% and time in warning of 0.1. Conclusion: Achieved performances highlight the feasibility of seizure forecasting in canine epilepsy. Significance: The ability to improve seizure forecasting provides promise for the development of EEG-triggered closed-loop seizure intervention systems for ambulatory implantation in patients with refractory epilepsy.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2017.2752081