Scalp EEG classification using deep Bi-LSTM network for seizure detection-Reference-Cited by-同舟云学术

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Scalp EEG classification using deep Bi-LSTM network for seizure detection

Published:2020-09 Issue: Volume:124 Page:103919
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Hu Xinmei,Yuan Shasha,Xu Fangzhou,Leng Yan,Yuan Kejiang,Yuan Qi

Funder

National Natural Science Foundation of China

Shandong Provincial Natural Science Foundation

Youth Innovative Research Team in University of Shandong Province, China

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference37 articles.

1. Epileptic seizure detection using cross-bispectrum of electroencephalogram signal;Mahmoodian;Seizure,2019

2. Epileptic seizure detection based on imbalanced classification and wavelet packet transform;Yuan;Seizure,2017

3. Patient-specific seizure detection in long-term EEG using wavelet decomposition;Kaleem;Biomed. Signal Process Contr.,2018

4. Epileptic seizure classifications of single-channel scalp EEG data using wavelet-based features and SVM;Janjarasjitt;Med. Biol. Eng. Comput.,2017

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1. Recording brain activity while listening to music using wearable EEG devices combined with Bidirectional Long Short-Term Memory Networks;Alexandria Engineering Journal;2024-12

2. Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection;International Journal of Neural Systems;2024-09-09

3. Advanced Epileptic Seizure Detection Using Deep Learning and Bayesian Optimization;SN Computer Science;2024-09-02

4. Seizure detection via deterministic learning feature extraction;Pattern Recognition;2024-09

5. Classification of epileptic seizures in EEG data based on iterative gated graph convolution network;Frontiers in Computational Neuroscience;2024-08-29

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