Prediction of Epileptic Seizures based on EEG Signal using CNN Model

Millions of patients suffer from epilepsy each year, a chronic nervous illness with a growing global prevalence. In a lot of situations, it might cause critical injuries or patient deaths. So, the automatic prediction of the epileptic seizure saves the patients from injury as well as from death. In this age of technological advancements, the internet of things (IoT) has started to offer a variety of solutions in the healthcare sector through the use of machine learning, deep learning, and cloud-based services. Internet of thing, Edge computing, and Cloud computing helps in the detection of such nervous disorders using deep learning and machine learning. Most of the models were developed preciously but there are many issues related to the prediction and security of the data of patients. This research study proposed an autonomous cloud-edge integrated seizure prediction model based on a convolutional neural network. The proposed model detects and classifying the epileptic seizure using an EEG dataset. The proposed model shows average training accuracy and average validation accuracy are 99.27% and 96.37%, respectively and the average losses for training and validation are 2.10 % and 14.23%, respectively. The result shows that overall accuracies and losses are better than the wellknown existing works. All implementation for seizure prediction is done in Python language.