Page 147 - MEGIN Book Of Abstracts - 2023

Hindustan Institute of Technology and Science, Padur, On-Scalp Optically Pumped Magnetometers versus
Kelambaakkam, Chengalpattu 603103, India; Department Cryogenic Magnetoencephalography for Diagnostic
of Information Systems, College of Computer Engineering & Evaluation of Epilepsy in School-aged Children
Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, (2022)
11942, Saudi Arabia; Department of Artificial Intelligence and
Data Science, Koneru Lakshmaiah Education Foundation, Feys, Odile; Corvilain, Pierre; Aeby, Alec; Sculier,
Vaddeswaram, A.P., India; United International University, Claudine; Holmes, Niall; Brookes, Matthew; Goldman,
Dhaka, Bangladesh Serge; Wens, Vincent; De Tiège, Xavier

ABSTRACT Magnetoencephalography (MEG) is now From the Departments of Neurology (O.F.), Pediatric Neurol-
widely used in clinical examinations and medical ogy (C.S., F.C.), Nuclear Medicine (S.G.), and Translational
research in many fields. Resting-state magnetoenceph- Neuroimaging (V.W., X.D.T.), CUB Hôpital Erasme, Université
alography-based brain network analysis can be used Libre de Bruxelles, Brussels, Belgium; Laboratory of Transla-
to study the physiological or pathological mechanisms tional Neuroimaging and Neuroanatomy (Laboratoire de
of the brain. Furthermore, magnetoencephalography Neuroimagerie et Neuroanatomie translationnelles) (LNT),
analysis has a significant reference value for the diagno- ULB Neuroscience Institute, Université Libre de Bruxelles, 808
sis of epilepsy. The scope of the proposed research is Lennik St, Brussels, Belgium (O.F., P.C., S.G., V.W., X.D.T.); Depart-
that this research demonstrates how to locate spikes ment of Pediatric Neurology, Hôpital Universitaire des Enfants
in the phase locking functional brain connectivity net- Reine Fabiola, Université Libre de Bruxelles, Brussels, Belgium
work of the Desikan-Killiany brain region division using (A.A.); and Sir Peter Mansfield Imaging Centre, School of Phys-
a neural network approach. It also improves detection ics and Astronomy, University of Nottingham, Nottingham,
accuracy and reduces missed and false detection rates. United Kingdom (N.H., M.B.)
The automatic classification of epilepsy encephalo-
magnetic signals can make timely judgments on the ABSTRACT Background Magnetoencephalography
patient's condition, which is of tremendous clinical (MEG) is an established method used to detect and
significance. The existing literature's research on the localize focal interictal epileptiform discharges (IEDs).
automatic type of epilepsy EEG signals is relatively Current MEG systems house hundreds of cryogenic
sufficient, but the research on epilepsy EEG signals is sensors in a rigid, one-size-fits-all helmet, which results
relatively weak. A full-band machine learning auto- in several limitations, particularly in children. Purpose
matic discrimination method of epilepsy brain mag- To determine if on-scalp MEG based on optically
netic spikes based on the brain functional connection pumped magnetometers (OPMs) alleviates the main
network is proposed. The four classifiers are compre- limitations of cryogenic MEG. Materials and Methods
hensively compared. The classifier with the best effect In this prospective single-center study conducted in
is selected, and the discrimination accuracy can reach a tertiary university teaching hospital, participants
93.8%. Therefore, this method has a good application underwent cryogenic (102 magnetometers, 204 planar
prospect in automatically identifying and labeling epi- gradiometers) and on-scalp (32 OPMs) MEG. The two
leptic spikes in magnetoencephalography. modalities for the detection and localization of IEDs
were compared. The t test was used to compare IED
Computational and mathematical methods in medicine amplitude and signal-to-noise ratio (SNR). Distributed
(2022), Vol. 2022 (35529257) (0 citations) source modeling was performed on OPM-based and
cryogenic MEG data. Results Five children (median age,
9.4 years [range, 5-11 years]; four girls) with self-limited
idiopathic (n = 3) or refractory (n = 2) focal epilepsy
were included. IEDs were identified in all five children
with comparable sensor topographies for both MEG
devices. IED amplitudes were 2.3 (7.2 of 3.1) to 4.6 (3.2

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