Authors:
Antonis Golfidis
1
;
Michael Vinos
2
;
3
;
Nikos Vassilopoulos
2
;
3
;
Eirini Papadaki
2
;
3
;
Irini Skaliora
1
;
2
;
3
and
Vassilis Cutsuridis
1
;
4
Affiliations:
1
Athens International Master’s Programme in Neurosciences, Department of Biology, National and Kapodistrian University of Athens, Athens, Greece
;
2
Department of History and Philosophy of Science, National and Kapodistrian University of Athens, Athens, Greece
;
3
Center for Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
;
4
School of Computer Science, University of Lincoln, Lincoln, U.K.
Keyword(s):
Machine Learning, Classification, HCTSA, Epilepsy, Animal, LFP, Endogenous Activity, Interictal Activity, Seizure like Activity.
Abstract:
Successful preictal, interictal and ictal activity discrimination is extremely important for accurate seizure detection and prediction in epileptology. Here, we introduce an algorithmic pipeline applied to local field potentials (LFPs) recorded from layers II/III of the primary somatosensory cortex of young mice for the classification of endogenous (preictal), interictal, and seizure-like (ictal) activity events using time series analysis and machine learning (ML) models. Using the HCTSA time series analysis toolbox, over 4000 features were extracted from the LFPs after applying over 7700 operations. Iterative application of correlation analysis and random-forest-recursive-feature-elimination with cross validation method reduced the dimensionality of the feature space to 22 features and 27 features, in endogenous-to-interictal events discrimination, and interictal-to-ictal events discrimination, respectively. Application of nine ML algorithms on these reduced feature sets showed prei
ctal activity can be discriminated from interictal activity by a radial basis function SVM with a 0.9914 Cohen kappa score with just 22 features, whereas interictal and seizure-like (ictal) activities can be discriminated by the same classifier with a 0.9565 Cohen kappa score with just 27 features. Our preliminary results show that ML application in cortical LFP recordings may be a promising research avenue for accurate seizure detection and prediction in focal epilepsy.
(More)