Neuro Detect: A Machine Learning and Deep Learning Framework for Epileptic Seizure Detection in Neurological Disorder

Abstract

Epilepsy is a brain disease that causes people to have seizures over and over again. It is very hard to diagnose and treat. Finding epileptic seizures early is very important for getting help right away and improving the patient's result. This paper discussed about a way to find epileptic seizures using a mix of signal processing methods as feature extraction techniques, such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), along with different machine learning algorithms. The suggested framework is made up of several steps. First, noise and other errors are taken out of the raw EEG data. Next, PCA and ICA are used to pull out important features from the signals that have already been processed. These features are then fed into a number of machine learning methods to help them classify the data. The proposed framework uses Support Vector Machine (SVM), Random Forest, Convolutional Neural Networks (CNN), Long short-term memory (LSTM), CNN with BiLSTM, and Gradient Boosting, which are all machine learning methods used for correctness of proposed model. These methods are used to divide the EEG data into groups of seizures and non-seizures. A sample with EEG readings from people with epilepsy is used to test how well the system works. This dataset is used to train and test the framework. Performance measures like sensitivity, specificity, and accuracy are used to judge how well it works. According to the results, the suggested framework is very good at finding epileptic seizures. It combines PCA for feature extraction with a number of machine learning methods. The framework's flexible design makes it easy to add new features and methods and change how it works.