EEG Signal Analysis Using Dynamic Time Warp Transformation and Kohonen’s Neural Network

1995 ◽  
pp. 249-252
Author(s):  
F. Cremer ◽  
L. P. J. Veelenturf
Keyword(s):  
Neural Network ◽  
Signal Analysis ◽  
Eeg Signal ◽  
Time Warp ◽  
Kohonen’S Neural Network ◽  
Dynamic Time ◽  
Eeg Signal Analysis

scholarly journals A One-Dimensional CNN-LSTM Model for Epileptic Seizure Recognition Using EEG Signal Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
Gaowei Xu ◽  
Tianhe Ren ◽  
Yu Chen ◽  
Wenliang Che
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Epileptic Seizure ◽  
Signal Analysis ◽  
Epileptic Seizures ◽  
Support Vector ◽  
Eeg Signal ◽  
Learning Methods ◽  
One Dimensional ◽  
Eeg Signal Analysis

Frequent epileptic seizures cause damage to the human brain, resulting in memory impairment, mental decline, and so on. Therefore, it is important to detect epileptic seizures and provide medical treatment in a timely manner. Currently, medical experts recognize epileptic seizure activity through the visual inspection of electroencephalographic (EEG) signal recordings of patients based on their experience, which takes much time and effort. In view of this, this paper proposes a one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) model for automatic recognition of epileptic seizures through EEG signal analysis. Firstly, the raw EEG signal data are pre-processed and normalized. Then, a 1D convolutional neural network (CNN) is designed to effectively extract the features of the normalized EEG sequence data. In addition, the extracted features are then processed by the LSTM layers in order to further extract the temporal features. After that, the output features are fed into several fully connected layers for final epileptic seizure recognition. The performance of the proposed 1D CNN-LSTM model is verified on the public UCI epileptic seizure recognition data set. Experiments results show that the proposed method achieves high recognition accuracies of 99.39% and 82.00% on the binary and five-class epileptic seizure recognition tasks, respectively. Comparing results with traditional machine learning methods including k-nearest neighbors, support vector machines, and decision trees, other deep learning methods including standard deep neural network and CNN further verify the superiority of the proposed method.

EEG signal analysis using deep learning: A systematic literature review

2021 ◽  
Author(s):  
Ghita Amrani ◽  
Amina Adadi ◽  
Mohammed Berrada ◽  
Zouhayr Souirti ◽  
Said Boujraf
Keyword(s):  
Deep Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Signal Analysis ◽  
Eeg Signal ◽  
Eeg Signal Analysis
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