Machine Learning Algorithms for Mouse LFP Data Classification in Epilepsy

Antonis Golfidis, Michael Vinos, Michael Vinos, Nikos Vassilopoulos, Nikos Vassilopoulos, Eirini Papadaki, Eirini Papadaki, Irini Skaliora, Irini Skaliora, Irini Skaliora, Vassilis Cutsuridis, Vassilis Cutsuridis

2023

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 preictal 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.

Paper Citation

in Harvard Style

Golfidis A., Vinos M., Vassilopoulos N., Papadaki E., Skaliora I. and Cutsuridis V. (2023). Machine Learning Algorithms for Mouse LFP Data Classification in Epilepsy. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS; ISBN 978-989-758-631-6, SciTePress, pages 36-47. DOI: 10.5220/0011625600003414

in Bibtex Style

@conference{biosignals23,
author={Antonis Golfidis and Michael Vinos and Nikos Vassilopoulos and Eirini Papadaki and Irini Skaliora and Vassilis Cutsuridis},
title={Machine Learning Algorithms for Mouse LFP Data Classification in Epilepsy},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={36-47},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011625600003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS
TI - Machine Learning Algorithms for Mouse LFP Data Classification in Epilepsy
SN - 978-989-758-631-6
AU - Golfidis A.
AU - Vinos M.
AU - Vassilopoulos N.
AU - Papadaki E.
AU - Skaliora I.
AU - Cutsuridis V.
PY - 2023
SP - 36
EP - 47
DO - 10.5220/0011625600003414
PB - SciTePress