Paper Info
Title
Electroencephalogram Signals Analysis by Fuzzy Classifiers based on Cumulative Mutual Information.
Abstract
New algorithm for EEG signals classification is proposed in this paper. The typical algorithm for signal classification includes two steps: preliminary transformation and classification. The preliminary transformation modifies investigated signals by the procedures of feature extraction and dimension reduction into data set accepted for the classification. This transformation causes the loss of some information that can improve the classification accuracy. New procedure of fuzzification is added in the preliminary transformation for the proposed algorithm. This procedure allows using the fuzzy classifier at the second step. The Fuzzy Random Forest is used for the classification of EEG signals in the proposed algorithm. The new procedure (fuzzification) in the preliminary transformation and new fuzzy classifier (Fuzzy Random Forest) allows increase the classification accuracy of the EEG signals. The efficiency of new algorithm is evaluated by two investigations. The first one focuses on epilepsy diagnostics and the second one aims at epileptics' seizure detection. The comparison with other studies and shows the increasing of the classification accuracy of EEG signals by the proposed algorithm.
Year
Venue
Keywords
2018
CEUR Workshop Proceedings-Series
Electroencephalogram (EEG),Fuzzification,Fuzzy Decision Tree,Fuzzy Random Forest
DocType
Volume
ISSN
Conference
2255
1613-0073
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Jan Rabcan102.37
Elena Zaitseva242.45
V. G. Levashenko3244.24