Survey on Epileptic Seizure Detection on Varied Machine Learning Algorithms

Abstract

Epilepsy is an unavoidable major persistent and critical neurological disorder that influences the human brain. Moreover, this is apparently distinguished via its recurrent malicious seizures. A seizure is a phase of synchronous, abnormal innervations of a neuron's population which might last from seconds to a few minutes. In addition, epileptic seizures are transient occurrences of complete or partial irregular unintentional body movements that combine with consciousness loss. As epileptic seizures rarely occurred in each patient, their effects based on physical communications, social interactions, and patients' emotions are considered, and treatment and diagnosis are undergone with crucial implications. Therefore, this survey reviews 65 research papers and states an important analysis on various machine-learning approaches adopted in each paper. The analysis of different features considered in each work is also done. This survey offers a comprehensive study on performance attainment in each contribution. Furthermore, the maximum performance attained by the works and the datasets used in each work is also examined. The analysis on features and the simulation tools used in each contribution is examined. At the end, the survey expanded with different research gaps and their problem which is beneficial to the researchers for promoting advanced future works on epileptic seizure detection.