Machine learning-based EEG signals classification model for epileptic seizure detection

被引:0
作者
Muhammad Bilal Aayesha
Muhammad Qureshi
Muhammad Shuaib Afzaal
Muhammad Qureshi
机构
[1] Shaheed Zulfikar Ali Bhutto Institute of Science and Technology,Department of Computer Science
[2] Stockholm University,Department of Computer and Systems Sciences
[3] University of Central Asia,Department of Computer Science, School of Arts and Sciences
来源
Multimedia Tools and Applications | 2021年 / 80卷
关键词
Machine learning; Epilepsy; Seizure detection; Signal processing; EEG; Classification;
D O I
暂无
中图分类号
学科分类号
摘要
The detection of epileptic seizures by classifying electroencephalography (EEG) signals into ictal and interictal classes is a demanding challenge, because it identifies the seizure and seizure-free states of an epileptic patient. In previous works, several machine learning-based strategies were introduced to investigate and interpret EEG signals for the purpose of their accurate classification. However, non-linear and non-stationary characteristics of EEG signals make it complicated to get complete information about these dynamic biomedical signals. In order to address this issue, this paper focuses on extracting the most discriminating and distinguishing features of seizure EEG recordings to develop an approach that employs both fuzzy-based and traditional machine learning algorithms for epileptic seizure detection. The proposed framework classifies unknown EEG signal segments into ictal and interictal classes. The model is validated using empirical evaluation on two benchmark datasets, namely the Bonn and Children’s Hospital of Boston-Massachusetts Institute of Technology (CHB-MIT) datasets. The obtained results show that in both cases, K-Nearest Neighbor (KNN) and Fuzzy Rough Nearest Neighbor (FRNN) give the highest classification accuracy scores, with improved sensitivity and specificity percentages.
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收藏
页码:17849 / 17877
页数:28
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