Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 80, Issue 12, Pages 17849-17877Publisher
SPRINGER
DOI: 10.1007/s11042-021-10597-6
Keywords
Machine learning; Epilepsy; Seizure detection; Signal processing; EEG; Classification
Ask authors/readers for more resources
This paper focuses on extracting 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 obtained results show that K-Nearest Neighbor (KNN) and Fuzzy Rough Nearest Neighbor (FRNN) give the highest classification accuracy scores, with improved sensitivity and specificity percentages.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available