FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3107776278> ?p ?o ?g. }

• W3107776278 abstract "Numerous computer automated diagnosis (CAD) systems have been proposed to detect epilepsy in electroencephalogram (EEG) signals. The aim of this paper is to look at multi-scaling properties obtained by multi-scale analysis (MSA) as main distinctive features to simultaneously distinguish between all categories of EEG signals that compose the popular database hosted by the department of epileptology, University of Bonn, Germany. Particularly, multi-scale analysis is employed to capture long-range properties of the EEG signal at different scales used to represent its short and long variations. Then, the obtained multi-scale properties are used to train four different classifiers; namely, k-nearest neighbor (k-NN), linear discriminant analysis (LDA), naïve Bayes (NB), and the support vector machine (SVM). Experimental results based on ten-fold cross-validation method show that each single classifier achieves 100% accuracy. In this respect, multi-scale properties are found to be effective as they outperformed existing works on the same database by achieving perfect accuracy to distinguish between all five distinct EEG categories. Overall, the obtained results are promising." @default.
• W3107776278 created "2020-12-07" @default.
• W3107776278 creator A5049507437 @default.
• W3107776278 date "2020-08-30" @default.
• W3107776278 modified "2023-09-26" @default.
• W3107776278 title "General Framework for Multi-Classification of EEG Signals Based on Multi-Scale Properties" @default.
• W3107776278 cites W1589606770 @default.
• W3107776278 cites W1969946769 @default.
• W3107776278 cites W1970917709 @default.
• W3107776278 cites W1979148805 @default.
• W3107776278 cites W1981211771 @default.
• W3107776278 cites W1988150589 @default.
• W3107776278 cites W1990441178 @default.
• W3107776278 cites W1996183177 @default.
• W3107776278 cites W1998711164 @default.
• W3107776278 cites W2001612033 @default.
• W3107776278 cites W2005305683 @default.
• W3107776278 cites W2005791255 @default.
• W3107776278 cites W2011714246 @default.
• W3107776278 cites W2016458295 @default.
• W3107776278 cites W2016998251 @default.
• W3107776278 cites W2023335485 @default.
• W3107776278 cites W2024633000 @default.
• W3107776278 cites W2030925257 @default.
• W3107776278 cites W2031675364 @default.
• W3107776278 cites W2042323927 @default.
• W3107776278 cites W2050209058 @default.
• W3107776278 cites W2053744708 @default.
• W3107776278 cites W2059016985 @default.
• W3107776278 cites W2065454702 @default.
• W3107776278 cites W2069115302 @default.
• W3107776278 cites W2071341607 @default.
• W3107776278 cites W2078760541 @default.
• W3107776278 cites W2081377128 @default.
• W3107776278 cites W2098149468 @default.
• W3107776278 cites W2102244548 @default.
• W3107776278 cites W2107541057 @default.
• W3107776278 cites W2107941496 @default.
• W3107776278 cites W2113662425 @default.
• W3107776278 cites W2122111042 @default.
• W3107776278 cites W2129920988 @default.
• W3107776278 cites W2140434576 @default.
• W3107776278 cites W2140785063 @default.
• W3107776278 cites W2154877864 @default.
• W3107776278 cites W2156909104 @default.
• W3107776278 cites W2246329527 @default.
• W3107776278 cites W2462271429 @default.
• W3107776278 cites W2560897477 @default.
• W3107776278 cites W2568407436 @default.
• W3107776278 cites W2592790850 @default.
• W3107776278 cites W2598587204 @default.
• W3107776278 cites W2606301878 @default.
• W3107776278 cites W2734768853 @default.
• W3107776278 cites W2739428828 @default.
• W3107776278 cites W2751033988 @default.
• W3107776278 cites W2763649037 @default.
• W3107776278 cites W2767334709 @default.
• W3107776278 cites W2769083668 @default.
• W3107776278 cites W2790950056 @default.
• W3107776278 cites W2792295722 @default.
• W3107776278 cites W2914534442 @default.
• W3107776278 cites W59945300 @default.
• W3107776278 doi "https://doi.org/10.1109/ccece47787.2020.9255822" @default.
• W3107776278 hasPublicationYear "2020" @default.
• W3107776278 type Work @default.
• W3107776278 sameAs 3107776278 @default.
• W3107776278 citedByCount "1" @default.
• W3107776278 countsByYear W31077762782023 @default.
• W3107776278 crossrefType "proceedings-article" @default.
• W3107776278 hasAuthorship W3107776278A5049507437 @default.
• W3107776278 hasConcept C118552586 @default.
• W3107776278 hasConcept C119857082 @default.
• W3107776278 hasConcept C121332964 @default.
• W3107776278 hasConcept C12267149 @default.
• W3107776278 hasConcept C124101348 @default.
• W3107776278 hasConcept C153180895 @default.
• W3107776278 hasConcept C154945302 @default.
• W3107776278 hasConcept C15744967 @default.
• W3107776278 hasConcept C2524010 @default.
• W3107776278 hasConcept C2778755073 @default.
• W3107776278 hasConcept C28490314 @default.
• W3107776278 hasConcept C33923547 @default.
• W3107776278 hasConcept C41008148 @default.
• W3107776278 hasConcept C52001869 @default.
• W3107776278 hasConcept C522805319 @default.
• W3107776278 hasConcept C52620605 @default.
• W3107776278 hasConcept C62520636 @default.
• W3107776278 hasConcept C69738355 @default.
• W3107776278 hasConcept C95623464 @default.
• W3107776278 hasConcept C99844830 @default.
• W3107776278 hasConceptScore W3107776278C118552586 @default.
• W3107776278 hasConceptScore W3107776278C119857082 @default.
• W3107776278 hasConceptScore W3107776278C121332964 @default.
• W3107776278 hasConceptScore W3107776278C12267149 @default.
• W3107776278 hasConceptScore W3107776278C124101348 @default.
• W3107776278 hasConceptScore W3107776278C153180895 @default.
• W3107776278 hasConceptScore W3107776278C154945302 @default.
• W3107776278 hasConceptScore W3107776278C15744967 @default.
• W3107776278 hasConceptScore W3107776278C2524010 @default.
• W3107776278 hasConceptScore W3107776278C2778755073 @default.

• next