Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning-Reference-Cited by-同舟云学术

Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning

Published:2022-10 Issue:10 Volume:41 Page:2879-2890
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Hirano Ryoji¹^ORCID,Emura Takuto²,Nakata Otoichi³,Nakashima Toshiharu³,Asai Miyako³^ORCID,Kagitani-Shimono Kuriko⁴,Kishima Haruhiko⁵^ORCID,Hirata Masayuki²

Affiliation:

1. CoMIT Department of Neurological Diagnosis and Restoration, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan

2. Ricoh, Osaka, Japan

3. Ricoh, Kanagawa, Japan

4. United Graduate School of Child Development, Osaka University, Suita, Japan

5. Department of Neurosurgery, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan

Funder

Collaborative Grant Between RICOH Corporation and the Department of Neurological Diagnosis and Restoration (NDR), Graduate School of Medicine, Osaka University

Japan Agency for Medical Research and Development

Grants-in-Aid for Scientific Research from KAKENHI

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/9906463/09772048.pdf?arnumber=9772048

Reference34 articles.

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2. Assessment of the Utility of Ictal Magnetoencephalography in the Localization of the Epileptic Seizure Onset Zone

3. Clinical MEG Analyses for Children with Intractable Epilepsy