Automatic classification of hyperkinetic, tonic, and tonic-clonic seizures using unsupervised clustering of video signals.

ABSTRACT:

Introduction

This study evaluated the accuracy of motion signals extracted from video monitoring data to differentiate epileptic motor seizures in patients with drug-resistant epilepsy. 3D near-infrared video was recorded by the Nelli^® seizure monitoring system (Tampere, Finland).

Methods

10 patients with 130 seizures were included in the training dataset, and 17 different patients with 98 seizures formed the testing dataset. Only seizures with unequivocal hyperkinetic, tonic, and tonic-clonic semiology were included. Motion features from the catch22 feature collection extracted from video were explored to transform the patients' videos into numerical time series for clustering and visualization.

Results

Changes in feature generation provided incremental di

SUBMITTER: Ojanen P 

PROVIDER: S-EPMC10652877 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature