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Publication Type : Conference Paper
Publisher : Journal of Physics: Conference Series
Source : Journal of Physics: Conference Series 2161 (1), 012055
Url : https://iopscience.iop.org/article/10.1088/1742-6596/2161/1/012055/meta
Campus : Amritapuri
School : School of Computing
Center : AI (Artificial Intelligence) and Distributed Systems
Department : Computer Science and Engineering
Year : 2022
Abstract : Epilepsy is a common neurological disease that affects more than 2 percent of the population globally. An imbalance in brain electrical activities causes unpredictable seizures, which eventually leads to epilepsy. Neurostimulators have the power to intervene in advance and avoid the occurrence of seizures. Its efficiency can be increased with the help of heuristics like advanced seizure prediction. Early identification of preictal state will help easy activation of neurostimulator on time. This research concentrates on the performance analysis of various machine learning algorithms on recorded EEG data. Through this study, we aim to find the best model, which can be used to create an ensemble model for better learning. This involves modeling and simulation of classical machine learning technique like Logistic regression, Naive Bayes model, K nearest neighbors Random Forest, and deep learning techniques like an Artificial neural network, Convolutional neural networks, Long short term memory, and Autoencoders. In this analysis, Random Forest and Long Short-Term Memory performed well among all models in terms of sensitivity and specificity.
Cite this Research Publication : HO Lekshmy, D Panickar, S Harikumar, "Comparative analysis of multiple machine learning algorithms for epileptic seizure prediction", Journal of Physics: Conference Series 2161 (1), 012055, 2022