Automated Epilepsy Seizure Detection from EEG Signals Using Deep CNN Model

2020 ◽  
pp. 15-30
Author(s):  
Saroj Kumar Pandey ◽  
Rekh Ram Janghel ◽  
Archana Verma ◽  
Kshitiz Varma ◽  
Pankaj Kumar Mishra
Keyword(s):  
Seizure Detection ◽  
Eeg Signals ◽  
Deep Cnn

scholarly journals An Improved Optimization Algorithm for Epileptic Seizure Detection in EEG Signals Using Random Forest Classifier

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 327-340
Author(s):  
A. Phraeson Gini ◽  
Dr.M.P. Flower Queen
Keyword(s):  
Random Forest ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
True Positive Rate ◽  
Intrinsic Mode Functions ◽  
Selection Algorithm ◽  
Eeg Signals ◽  
Efficient Detection ◽  
Positive Rate ◽  
Electroencephalogram Eeg

Epilepsy is a psychiatric condition that has serious consequences for the human brain. The Electroencephalogram (EEG) may reveal a pattern that tells physicians whether an epileptic seizure is likely to occur again. EEG testing may also help the physician exclude other conditions that mimic epilepsy as a reason for the seizure. Now-a-days the researchers are showing much interest in these seizure detection because of its significance in epileptic detection. This paper is addressing an efficient soft computing framework for seizure detection from the EEG signal. The proposed pipeline of work is having the state-of-art as the possibility of achieving the maximum accuracy. The spectral features extracted from the Intrinsic mode functions (IMF) of EEG samples and it is directing the proposed flow towards the efficient detection of seizure and also the random forest algorithm based a convulsion classification is reliable for because of its learning behavior from the huge number of known dataset. The feature selection algorithm in this proposed work is stimulating the overall work towards the maximum true positive rate. This work is implemented on MATLAB platform and dataset were downloaded from the universal database such as Bonn university database. The results obtained from the proposed approach is showing the truthfulness of the approach introduced here.

scholarly journals Epileptic seizure detection from EEG signals using logistic model trees

2016 ◽  
Vol 3 (2) ◽  
pp. 93-100 ◽  
Author(s):  
Enamul Kabir ◽  
Siuly ◽  
Yanchun Zhang
Keyword(s):  
Epileptic Seizure ◽  
Logistic Model ◽  
Seizure Detection ◽  
Eeg Signals ◽  
Model Trees ◽  
Epileptic Seizure Detection
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