RT info:eu-repo/semantics/article
T1 Virtual reality and machine learning in the automatic photoparoxysmal response detection
A1 Moncada, Fernando
A1 Martín, Sofía
A1 González, Víctor M.
A1 Álvarez, Víctor M.
A1 García López, Beatriz
A1 Gómez Menéndez, Ana Isabel
A1 Villar, José R.
K1 Electroencefalogram
K1 Virtual reality
K1 Photoparoxysmal response
K1 Machine learning
K1 Neurología
K1 Neurology
K1 Fisiología
K1 Physiology
K1 Salud
K1 Health
AB Photosensitivity, in relation to epilepsy, is a genetically determined condition in which patients have epileptic seizures of different severity provoked by visual stimuli. It can be diagnosed by detecting epileptiform discharges in their electroencephalogram (EEG), known as photoparoxysmal responses (PPR). The most accepted PPR detection method—a manual method—considered as the standard one, consists in submitting the subject to intermittent photic stimulation (IPS), i.e. a flashing light stimulation at increasing and decreasing flickering frequencies in a hospital room under controlled ambient conditions, while at the same time recording her/his brain response by means of EEG signals. This research focuses on introducing virtual reality (VR) in this context, adding, to the conventional infrastructure a more flexible one that can be programmed and that will allow developing a much wider and richer set of experiments in order to detect neurological illnesses, and to study subjects’ behaviours automatically. The loop includes the subject, the VR device, the EEG infrastructure and a computer to analyse and monitor the EEG signal and, in some cases, provide feedback to the VR. As will be shown, AI modelling will be needed in the automatic detection of PPR, but it would also be used in extending the functionality of this system with more advanced features. This system is currently in study with subjects at Burgos University Hospital, Spain.
PB Springer
SN 0941-0643
YR 2022
FD 2022
LK http://hdl.handle.net/10259/8567
UL http://hdl.handle.net/10259/8567
LA eng
NO Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
DS Repositorio Institucional de la Universidad de Burgos
RD 24-nov-2024