ULTRACEPT researcher Dr Vassilis Cutsuridis is a Senior Lecturer in Computer Science, and a member of the Machine Learning research group at the University of Lincoln. He recently attended the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) to present his paper titled ‘Machine Learning Algorithms for Mouse LFP Data Classification in Epilepsy’. The conference took place 16th to the 18th February 2023 in Lisbon, Portugal.

The purpose of BIOSTEC is to bring together researchers and practitioners, including engineers, biologists, health professionals and informatics/computer scientists, interested in both theoretical advances and applications of information systems, artificial intelligence, signal processing, electronics and other engineering tools in knowledge areas related to biology and medicine. BIOSTEC is composed of five co-located conferences, each specialized in a different knowledge area.

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.