Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks

Abstract

We propose a novel neural network architecture, SZTrack, to detect and track the spatio-temporal propagation of seizure activity in multichannel EEG. SZTrack combines a convolutional neural network encoder operating on individual EEG channels with recurrent neural networks to capture the evolution of seizure activity. Our unique training strategy aggregates individual electrode level predictions for patient-level seizure detection and localization. We evaluate SZTrack on a clinical EEG dataset of 201 seizure recordings from 34 epilepsy patients acquired at the Johns Hopkins Hospital. Our network achieves similar seizure detection performance to state-of-the-art methods and provides valuable localization information that has not previously been demonstrated in the literature. We also show the cross-site generalization capabilities of SZTrack on a dataset of 53 seizure recordings from 14 epilepsy patients acquired at the University of Wisconsin Madison. SZTrack is able to determine the lobe and hemisphere of origin in nearly all of these new patients without retraining the network. To our knowledge, SZTrack is the first end-to-end seizure tracking network using scalp EEG.

Seizure onset zone (SOZ) identification using effective brain connectivity of epileptogenic networks

“…This was demonstrated in [33], where a supervised support vector machines (SVM) model using a combination of known biomarkers extracted from a large cohort of 82 epileptic patients predicted SOZ with an AUC of 0.79. The SZLoc deep neural network architecture proposed by [34] also revealed the capability to build an end-to-end neural network adept at automatic feature extraction, achieving superior predictability using non-invasive scalp EEG recordings with a mean accuracy of 71.1%. Building upon the successful demonstration of these methods, our goal is to enhance knowledge by developing a supervised ML model that utilizes EC-based graph centrality measures as an alternative set of features.…”

Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages

“…Graph convolutional neural network (GCNN) is a deep neural network classification model capable of handling multichannel EEG signal analysis (Craley et al, 2022). It is an improvement of convolutional neural networks (CNN) and can preserve richer connection information than 2D or 3D matrices by considering EEG signals to be nodes in a topological graph and representing the relationships between them using edges (Lian et al, 2020).…”