Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
Article Fingerprint
ReserarchID
334WB
Epilepsy is considered one of the common medical and social disorders with unique characteristics. EEG signal was used for the classification and detection of epilepsy. This study proposed epilepsy classification without signal decomposition, as well as other algorithms used for decomposing the EEG signal to sub-bands like discrete wavelet transform (DWT) and dual-tree complex wavelet transform (DT-CWT). Descriptive comparisons were done between results for EEG signals with/without decomposition. The proposed algorithm includes the study of the extracted features and using machine learning kernels as Support Vector Machine (SVM) and bagged tree to achieve the optimal values of (accuracy-specificity-sensitivity and execution time). Results show that adding the line length to the group of features, the accuracy increased to 99.4%. By employing decomposing the EEG signal, the accuracy could be raised to99.875 % even after reducing the number of features to only three features. These features are line length, STD, and mean. This study proposed different algorithms with minimum features for epilepsy classification and localization with optimum execution time.
Mostafa A. Abd-ElBaset. 2019. \u201cStudy of EEG Signal for Epilepsy Detection and Localization Using Bagged Tree and SVM Algorithms\u201d. Global Journal of Medical Research - K: Interdisciplinary GJMR-K Volume 19 (GJMR Volume 19 Issue K7): .
Crossref Journal DOI 10.17406/gjmra
Print ISSN 0975-5888
e-ISSN 2249-4618
The methods for personal identification and authentication are no exception.
The methods for personal identification and authentication are no exception.
Neural Networks and Rules-based Systems used to Find Rational and
A Comparative Study of the Effeect of Promotion on Employee
The Problem Managing Bicycling Mobility in Latin American Cities: Ciclovias
Impact of Capillarity-Induced Rising Damp on the Energy Performance of
Epilepsy is considered one of the common medical and social disorders with unique characteristics. EEG signal was used for the classification and detection of epilepsy. This study proposed epilepsy classification without signal decomposition, as well as other algorithms used for decomposing the EEG signal to sub-bands like discrete wavelet transform (DWT) and dual-tree complex wavelet transform (DT-CWT). Descriptive comparisons were done between results for EEG signals with/without decomposition. The proposed algorithm includes the study of the extracted features and using machine learning kernels as Support Vector Machine (SVM) and bagged tree to achieve the optimal values of (accuracy-specificity-sensitivity and execution time). Results show that adding the line length to the group of features, the accuracy increased to 99.4%. By employing decomposing the EEG signal, the accuracy could be raised to99.875 % even after reducing the number of features to only three features. These features are line length, STD, and mean. This study proposed different algorithms with minimum features for epilepsy classification and localization with optimum execution time.
We are currently updating this article page for a better experience.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
