N. Saranya, Park College of Engineering and Technology, Coimbatore, TamilNadu, India.
D. Karthika Renuka, PSG College of Technology, Coimbatore, TamilNadu, India.
International Journal of Advanced Information and Communication Technology
Received On : March 12, 2020
Revised On :April 18, 2020
Accepted On : May 30, 2020
Published On : July 05, 2020
Volume 07, Issue 07
Pages : 109-114
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.
Keywords
Predictive analytics; Prediction models; Machine learning; Classifications.
Cite this article
N. Saranya and D. Karthika Renuka, “ A Survey on Epilepsy Seizure Detection Using Machine Learning Technique, ” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp. 109–114, July. 2020.
Copyright
© 2020 N. Saranya and D. Karthika Renuka. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.