A Survey on Epilepsy Seizure Detection Using Machine Learning Technique

Saranya N,Karthika Renuka D

doi:10.46532/ijaict-2020024

Abstract

Epilepsy, One of the most prevalent neurological disorder. Its a chronic condition is characterized by voluntary, unpredictable, and recurrent seizures that affects millions of individuals worldwide. A brief alteration in normal brain function that affects the health of patients occurs in this chronic condition. Detection of epileptic seizures before the start of the onset is beneficial. Recent studies have suggested approaches to machine learning that automatically execute those diagnostic tasks by integrating statistics and computer science. Machine learning, an application of AI (Artificial Intelligence) technology, allows a machine to learn something new automatically and thereby improve its output through meaningful data. For the prediction of epileptic seizures from electroencephalogram (EEG) signals, machine learning techniques and computational methods are used. There is a vast amount of medical data available today about the disease, its symptoms, causes of illness and its effects. But this data is not analyzed properly to predict or to study a disease. The objective of this paper is to provide detailed versions of machine learning predictive models for predicting epilepsy seizure detection and describing several types of predictive models and their applications in the field of healthcare. So that seizures can be predicted earlier before it occurs, it will be useful for epilepsy patients to improve their safety and quality of their life.

Highlights

Epilepsy it makes difficulty for patients to live a normal life because it is difficult to predict when seizures will occur
The objective of this paper is to provide detailed versions of machine learning predictive models for predicting epilepsy seizure detection and describing several types of predictive models and their applications in the field of healthcare
The aim of this paper is to give a detailed version of predictive models and describing various types of predictive models, Applications of machine learning are significantly seen on health and biological data sets for better outcomes [1,5]

Summary

Introduction

Epilepsy it makes difficulty for patients to live a normal life because it is difficult to predict when seizures will occur. A huge amount of medical data is available today regarding the disease, their symptoms, reasons for illness, and their effects on health. This data is not analyzed properly to predict or to study a disease. Machine learning has been significantly applied to discover sensible and meaningful patterns from different domain datasets [8]. Applications of machine learning can be seen on brain datasets for seizure detection, epilepsy lateralization, differentiating seizure sates, and localization [4,6]. Ictal State : That begins with the onset of the seizure and ends with an attack

Postictal State : A state that starts after ictal state

Steps To Develop Predictive Model

Prediction System Model for Predicting Epilepsy Disease

Literature review

14 Early Prediction Of

Conclusion