Version 1
: Received: 7 June 2023 / Approved: 8 June 2023 / Online: 8 June 2023 (10:13:28 CEST)
How to cite:
Alizadeh, G.; Yousefi Rezaii, T.; Meshgini, S. Automatic Epileptic Seizure Prediction Based on Convolutional Neural Network and EEG Signal. Preprints2023, 2023060623. https://doi.org/10.20944/preprints202306.0623.v1
Alizadeh, G.; Yousefi Rezaii, T.; Meshgini, S. Automatic Epileptic Seizure Prediction Based on Convolutional Neural Network and EEG Signal. Preprints 2023, 2023060623. https://doi.org/10.20944/preprints202306.0623.v1
Alizadeh, G.; Yousefi Rezaii, T.; Meshgini, S. Automatic Epileptic Seizure Prediction Based on Convolutional Neural Network and EEG Signal. Preprints2023, 2023060623. https://doi.org/10.20944/preprints202306.0623.v1
APA Style
Alizadeh, G., Yousefi Rezaii, T., & Meshgini, S. (2023). Automatic Epileptic Seizure Prediction Based on Convolutional Neural Network and EEG Signal. Preprints. https://doi.org/10.20944/preprints202306.0623.v1
Chicago/Turabian Style
Alizadeh, G., Tohidy Yousefi Rezaii and Saeed Meshgini. 2023 "Automatic Epileptic Seizure Prediction Based on Convolutional Neural Network and EEG Signal" Preprints. https://doi.org/10.20944/preprints202306.0623.v1
Abstract
Epilepsy is a neurological disorder that affects approximately 1% of the world's population. To diagnose and estimate the occurrence of epilepsy, the analysis of recorded brain activity is performed by a neurologist, which is not only time-consuming and tedious but also occasionally accompanied by human error. Therefore, in recent decades, researchers have aimed to unravel an approach for designing and building an automated method for diagnosing and estimating the occurrence of epilepsy. Accordingly, the present study proposed two new-fangled ways based on brain signals and a convolutional neural network (CNN). Moreover, this research implements a CNN with a sequential three-layer structure. Numerous experiments were performed, and the accuracy of estimating epilepsy using the developed methods was achieved at 95% without feedback and 97% with feedback. The proposed methods were proven to be more accurate than the previous techniques and can be employed as a physician's assistant once entering the field of operation.
Keywords
Epilepsy; Electroencephalogram; Convolutional neural networks; Brain signal integral; Brain signal derivative
Subject
Engineering, Bioengineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
