Automated Epileptic Seizure Detection from EEG signals using Deep CNN model
DOI:
https://doi.org/10.59367/5acxpc55Abstract
Epilepsy is a condition that affects the brain and is characterized by irregular disruptions in the
regular functioning of the brain. Epileptic seizures affect about one percent of the human
population. At some point in their lives, about ten percent of the population in the United States
experiences at least one seizure. The process of automated seizure detection from EEG signals for
epilepsy using Convolutional Neural Networks (CNN) entails the training of a deep learning model
to discern seizure-associated patterns within brainwave data. CNN receives EEG signals, which
represent electrical activity in the brain, and it acquires the ability to differentiate between states
of normalcy and seizures. Utilizing CNN's capability to autonomously extract and acquire
knowledge of features from unprocessed EEG data enhances the precision of detection. The
implementation of automation improves the efficiency and dependability of seizure diagnosis, which
may facilitate prompt medical intervention. This study introduces a new and innovative deep
Convolutional Neural Network model with back-propagation methods. The model is designed to
classify EEG signals into three specific classes: pre-ictal, normal, and seizure. The experiment is
conducted on a publicly accessible benchmark database from Bonn University. The model's
performance was assessed by measuring its sensitivity, specificity, and accuracy by 10-fold crossvalidation.
The acquired experimental results include an accuracy of 97.33%, sensitivity of 96.00%,
and specificity of 98%. These results are then compared to those of other existing literature.
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