Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals

Abstract

Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This paper proposes a hybrid optimization-controlled ensemble classifier comprising the AdaBoost classifier, random forest (RF) classifier, and the decision tree (DT) classifier for the automatic analysis of an EEG signal dataset to predict an epileptic seizure. The EEG signal is pre-processed initially to make it suitable for feature selection. The feature selection process receives the alpha, beta, delta, theta, and gamma wave data from the EEG, where the significant features, such as statistical features, wavelet features, and entropy-based features, are extracted by the proposed hybrid seek optimization algorithm. These extracted features are fed forward to the proposed ensemble classifier that produces the predicted output. By the combination of corvid and gregarious search agent characteristics, the proposed hybrid seek optimization technique has been developed, and is used to evaluate the fusion parameters of the ensemble classifier. The suggested technique’s accuracy, sensitivity, and specificity are determined to be 96.6120%, 94.6736%, and 91.3684%, respectively, for the CHB-MIT database. This demonstrates the effectiveness of the suggested technique for early seizure prediction. The accuracy, sensitivity, and specificity of the proposed technique are 95.3090%, 93.1766%, and 90.0654%, respectively, for the Siena Scalp database, again demonstrating its efficacy in the early seizure prediction process.

A robust seizure detection and prediction method with feature selection and spatio-temporal casual neural network model

“…Table 10 is the comparison results of the LOOCVbased seizure prediction methods on Siena and Kaggle datasets. In Siena dataset, [58] uses statistical, wavelet, and entropy features and an ensemble classifier that consists of the Adaboost, random forest and DT. It achieves the SEN of 93.18%, which is 6.82% lower than our work.…”

EpiNet: A Hybrid Machine Learning Model for Epileptic Seizure Prediction using EEG Signals from a 500 Patient Dataset

“…Kapoor et al [3] introduce an innovative seizure prediction method using ensemble classifiers and hybrid search optimization, achieving a high accuracy of 96.61% on the CHB-MIT database. Their approach addresses existing limitations, sets a standard for researchers, and explores COVID-19-related data applications for enhanced seizure prediction.…”

Predicting the Posture of High-Rise Building Machines Based on Multivariate Time Series Neural Network Models

“…NNs possess linear and nonlinear fitting capabilities, making them adept at handling such data. Notably, NN technology has shown promising results in prediction tasks [ 27 , 28 , 29 , 30 ]. It is important to note, though, that these neural networks don’t do as well when they have to deal with tasks that use MTS class information and simulations that have strong back-and-forth correlations.…”