基于机器学习的癫痫发作早期检测系统

Mrs. Mayuri Tushar Deshmukh, Dr. Shekhar R Suralkar
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引用次数: 0

摘要

癫痫病发作被认为是当今最明显的神经系统疾病,它可以困扰所有年龄的人。全世界约有60亿人患有癫痫。脑电图(EEG)信号最常用于分析和检测癫痫发作。由于脑电信号中含有大量杂散伪影信息,因此许多研究者都在尝试支持自动结构来进行完整的特征提取。本文综述了流行的癫痫检测方法,并对所提出的k -最近邻(KNN)、支持向量机(SVM)和区域神经网络(RNN)算法进行了性能分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early Detection System for Epileptic Seizures By Using Machine Learning
-An epileptic seizure is considered the most conspicuous neurological disorder nowadays that can distress to all ages, people.Around 6 decades million people all over the world are travail from epilepsy.Electroencephalograph (EEG) signals are most commonly used for the analysis and detection ofseizures.As EEG signal contains anenormous amount of clatterartifact-included information, so many researchers are trying to support automatic structures for completefeature extraction. This paper provides areview of popular seizure detection methods and performance analysis of the proposed K-Nearest neighbor (KNN), Support Vector Machine(SVM), and Regional Neural Network(RNN) algorithms.
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