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

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

Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, these analyses are manually performed by neurophysi...

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Bibliographic Details
Main Authors: Asai, M. (Author), Emura, T. (Author), Hirano, R. (Author), Hirata, M. (Author), Kagitani-Shimono, K. (Author), Kishima, H. (Author), Nakashima, T. (Author), Nakata, O. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:
Automation
Brain mapping
Classification networks
Cost effectiveness
Deep learning
Dipole analysis
Epilepsy
Equivalent current dipole
Estimation
Fully automated
Image segmentation
Images segmentations
Magnetoencephalography
Magnetoencephalography (MEG)
Neurology
Semantic Segmentation
Semantics
Sensors
Spike detection
Training data
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