A Multi-Channel Feature Fusion CNN-Bi-LSTM Epilepsy EEG Classification and Prediction Model Based on Attention Mechanism-Reference-Cited by-同舟云学术

A Multi-Channel Feature Fusion CNN-Bi-LSTM Epilepsy EEG Classification and Prediction Model Based on Attention Mechanism

Published:2023 Issue: Volume:11 Page:62855-62864
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ma Yahong¹^ORCID,Huang Zhentao¹^ORCID,Su Jianyun²^ORCID,Shi Hangyu²^ORCID,Wang Dong³^ORCID,Jia Shanshan³,Li Weisu¹^ORCID

Affiliation:

1. School of Electronic Information, Xijing University, Xi’an, China

2. Neurosurgery Department, Affiliate Children’s Hospital, Xi’an Jiaotong University, Shaanxi, Xi’an, China

3. Department of Neurology, Affiliate Children’s Hospital, Xi’an Jiaotong University, Shaanxi, Xi’an, China

Funder

General Projects of Shaanxi Science and Technology Plan

Shaanxi Province Innovation Capacity Support Program

Xijing University Special Talent Research Fund

Research Project of Affiliate Children’s Hospital of Xi’an Jiaotong University in 2020

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10156816.pdf?arnumber=10156816

Reference27 articles.

1. Analysis of EEG Signals for Detection of Epileptic Seizure Using Hybrid Feature Set

2. Application of machine learning to epileptic seizure onset detection and treatment;shoeb,2009

3. A Multiclass EEG Signal Classification Model using Spatial Feature Extraction and XGBoost Algorithm

4. Epileptic seizure prediction using Pearson’s product-moment correlation coefficient of a linear classifier from generalized Gaussian modeling;quintero-rincon;arXiv 2006 01359,2020

5. Developing a mobile epilepsy management application integrated with an electronic health record for effective seizure management

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