Matches in SemOpenAlex for { <https://semopenalex.org/work/W3103261193> ?p ?o ?g. }
Showing items 1 to 92 of
92
with 100 items per page.
- W3103261193 abstract "A stroke occurs due to circulatory disorders in the brain that can cause severe disability. Therefore, it requires rehabilitation. One of the instruments used in monitoring post-stroke patients is the Electroencephalogram (EEG). However, EEG signals generated from multiple channels often experience data redundancy, affecting the computational time load and accuracy. Therefore, it needs to reduce the dimensions of the data of the EEG signal. This research proposed a model to classify post-stroke patients based on EEG signals that used Wavelet, Principal Component Analysis (PCA), and Convolutional Neural Networks (CNN). Wavelet transform is used to extract EEG signals into Delta, Alpha, Theta, and Mu waves. PCA works to remove some signal of multi-channel by selecting the number of components. Meanwhile, classification used one dimension Convolutional Neural Network (CNN). Experimental results gave using PCA with 45 components produced an accuracy of 93.33% compared without using PCA, which results in an accuracy of 86.66%. Besides, optimization models using AdaDelta provided higher accuracy compared to Adam optimization models." @default.
- W3103261193 created "2020-11-23" @default.
- W3103261193 creator A5029467167 @default.
- W3103261193 creator A5050478717 @default.
- W3103261193 creator A5082508074 @default.
- W3103261193 date "2020-09-23" @default.
- W3103261193 modified "2023-10-02" @default.
- W3103261193 title "Multivariate EEG Signal Using PCA and CNN in Post-Stroke Classification" @default.
- W3103261193 cites W2005791255 @default.
- W3103261193 cites W2086983697 @default.
- W3103261193 cites W2091150634 @default.
- W3103261193 cites W2289714406 @default.
- W3103261193 cites W2290410864 @default.
- W3103261193 cites W2374662404 @default.
- W3103261193 cites W2519140045 @default.
- W3103261193 cites W2534799678 @default.
- W3103261193 cites W2604290265 @default.
- W3103261193 cites W2620050178 @default.
- W3103261193 cites W2734526165 @default.
- W3103261193 cites W2775778209 @default.
- W3103261193 cites W2777450395 @default.
- W3103261193 cites W2909991941 @default.
- W3103261193 cites W2936427532 @default.
- W3103261193 cites W2946520801 @default.
- W3103261193 cites W2981687513 @default.
- W3103261193 cites W3003480090 @default.
- W3103261193 cites W3004168952 @default.
- W3103261193 cites W3014168629 @default.
- W3103261193 cites W3019105926 @default.
- W3103261193 doi "https://doi.org/10.1109/fortei-icee50915.2020.9249880" @default.
- W3103261193 hasPublicationYear "2020" @default.
- W3103261193 type Work @default.
- W3103261193 sameAs 3103261193 @default.
- W3103261193 citedByCount "4" @default.
- W3103261193 countsByYear W31032611932021 @default.
- W3103261193 countsByYear W31032611932022 @default.
- W3103261193 countsByYear W31032611932023 @default.
- W3103261193 crossrefType "proceedings-article" @default.
- W3103261193 hasAuthorship W3103261193A5029467167 @default.
- W3103261193 hasAuthorship W3103261193A5050478717 @default.
- W3103261193 hasAuthorship W3103261193A5082508074 @default.
- W3103261193 hasConcept C111919701 @default.
- W3103261193 hasConcept C118552586 @default.
- W3103261193 hasConcept C119857082 @default.
- W3103261193 hasConcept C152124472 @default.
- W3103261193 hasConcept C153180895 @default.
- W3103261193 hasConcept C154945302 @default.
- W3103261193 hasConcept C15744967 @default.
- W3103261193 hasConcept C161584116 @default.
- W3103261193 hasConcept C196216189 @default.
- W3103261193 hasConcept C199360897 @default.
- W3103261193 hasConcept C27438332 @default.
- W3103261193 hasConcept C2779843651 @default.
- W3103261193 hasConcept C28490314 @default.
- W3103261193 hasConcept C41008148 @default.
- W3103261193 hasConcept C47432892 @default.
- W3103261193 hasConcept C522805319 @default.
- W3103261193 hasConcept C81363708 @default.
- W3103261193 hasConceptScore W3103261193C111919701 @default.
- W3103261193 hasConceptScore W3103261193C118552586 @default.
- W3103261193 hasConceptScore W3103261193C119857082 @default.
- W3103261193 hasConceptScore W3103261193C152124472 @default.
- W3103261193 hasConceptScore W3103261193C153180895 @default.
- W3103261193 hasConceptScore W3103261193C154945302 @default.
- W3103261193 hasConceptScore W3103261193C15744967 @default.
- W3103261193 hasConceptScore W3103261193C161584116 @default.
- W3103261193 hasConceptScore W3103261193C196216189 @default.
- W3103261193 hasConceptScore W3103261193C199360897 @default.
- W3103261193 hasConceptScore W3103261193C27438332 @default.
- W3103261193 hasConceptScore W3103261193C2779843651 @default.
- W3103261193 hasConceptScore W3103261193C28490314 @default.
- W3103261193 hasConceptScore W3103261193C41008148 @default.
- W3103261193 hasConceptScore W3103261193C47432892 @default.
- W3103261193 hasConceptScore W3103261193C522805319 @default.
- W3103261193 hasConceptScore W3103261193C81363708 @default.
- W3103261193 hasLocation W31032611931 @default.
- W3103261193 hasOpenAccess W3103261193 @default.
- W3103261193 hasPrimaryLocation W31032611931 @default.
- W3103261193 hasRelatedWork W1977523676 @default.
- W3103261193 hasRelatedWork W2085553065 @default.
- W3103261193 hasRelatedWork W2380927352 @default.
- W3103261193 hasRelatedWork W2541950815 @default.
- W3103261193 hasRelatedWork W2767651786 @default.
- W3103261193 hasRelatedWork W2907827771 @default.
- W3103261193 hasRelatedWork W3048981730 @default.
- W3103261193 hasRelatedWork W3178621026 @default.
- W3103261193 hasRelatedWork W4211209597 @default.
- W3103261193 hasRelatedWork W2137598809 @default.
- W3103261193 isParatext "false" @default.
- W3103261193 isRetracted "false" @default.
- W3103261193 magId "3103261193" @default.
- W3103261193 workType "article" @default.