Life science
Clinical examination for epilepsy

Automated neuromagnetic examinationof epilepsy using deep learning
Department of Neurological Diagnosis and Restoration, Graduate School of Medicine
Specially Appointed Professor Masayuki Hirata

https://researchmap.jp/hiratty?lang=en

Visiting Researcher Ryoji Hirano				

https://researchmap.jp/hirapi105911784?lang=en▍ Background & Results
MEG can measure brain activity more accurately than EEG bymeasuring the magnetism generated by electrical activity in thebrain, and is used for clinical eaminations to evaluate epilepticactivity. MEG can non-invasively and more accurately investigate
localization of epileptic activity in the brain. It is also important asa pre-surgical examination for epilepsy treatment, especially whenaccurate diagnosis of the location of epilepsy is required. However,interpreting epileptic waveforms is complex, so until now, medical
doctors had to spend hours analyzing them. Therefore, despite its
high performance, MEG has only been installed in university hospitals and has not been widely adopted. In addition, an previous
study reported that it takes an average of 8 hours for analyses,
which has been a major burden for medical doctors. In this study,
we developed a system that can fully automatically analyze thelocalization of epileptic activity by not only accurately detecting
epileptic waves but also their time using two types of deep learning. We used two types of deep learning methods and made themlearn more than 400 test results analyzed by medical doctors. As aresult, we were able to achieve analytical performance equivalent
to that of medical doctors. Furthermore, by using the test resultsfrom multiple facilities, we were able to improve the generalization
performance. Until now, medical doctors had to spend hours foranalyses, but this analysis time is no longer required, and it is expected that the burden on doctors will be greatly reduced. The detection of epileptic waves is complex, and there is an issue that theinterpretation differs depending on doctors, but this method offers
an advantage that the quality of the test results can be homogenized.

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;;(<= '(>Magnetoencephalography (MEG) of epilepsy can accurately
and non-invasively locate epileptic activity. This is one of important
examinations for pre-surgical evaluation in epilepsy treatment.
However, detection of epileptic waveforms is complex, requiring
hours of analysis by specialists. MEG has not been widely adopted
despite its high performance. In this study, we developed a new
method for detecting epileptic waveforms using deep learning.
This system learned epileptic waveforms detected by medical doctors. We were able to fully automate MEG analyses. This systemeliminates the need for long hours of analysis and achieves performance comparable to medical doctors.

due to epileptic seizures, and applications to screening epilepsy
are also expected.

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Life science

Abstract

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!"#$"%&'&()%*%"&+),-.C,"D(<&()%*)>*!C'&()&"$C),'=*=)<'=(E'&()%*)>*"C(="C&(<*'<&(F(&GA report by automated examination▍ Significance of the research and Future perspectiveThis research is expected to popularize epilepsy MEG, whichused to take time to analyze, and enable accurate epilepsy diagnosis non-invasively. This method is also expected to be applied tothe automation of EEG analyses. Furthermore, it has recently beenfound that a certain proportion of cognitive decline in the elderly is

P a t e n t Japan Patent Application No. 2019-197953, JP2021069930, JP2021069929, Japan Patent Application No.2023-083473
Hirano, Ryoji; Emura, Takuto; Nakata, Otoichi et al. Fully-automated spike detection and dipole analysis of epileptic MEG using deep learning. IEEETr e a t i s eTransactions on Medical Imaging. 2022, 41(10), 2879-2890. doi: 10.1109/TMI.2022.3173743
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L https://www.med.osaka-u.ac.jp/pub/ndr/en/
K e y w o r d epilepsy, MEG, deep learning, clinical examination