Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation

Computer-Aided Intracranial EEG Signal Identification Method Based on a Multi-Branch Deep Learning Fusion Model and Clinical Validation

Surgical intervention or the control of drug-refractory epilepsy requires accurate analysis of invasive inspection intracranial EEG (iEEG) data. A multi-branch deep learning fusion model is proposed to identify epileptogenic signals from the epileptogenic area of the brain. The classical approach ex...

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Bibliographic Details
Main Authors: Yiping Wang, Yang Dai, Zimo Liu, Jinjie Guo, Gongpeng Cao, Mowei Ouyang, Da Liu, Yongzhi Shan, Guixia Kang, Guoguang Zhao
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Brain Sciences
Subjects:
intracranial EEG (iEEG)
SEEG
epileptogenic signals identification
multi-branch deep learning fusion
Online Access:https://www.mdpi.com/2076-3425/11/5/615

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