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- W3043565675 abstract "Seizures are abnormal activities that occur within the brain. Seizures are episodes that may differ from short periods to long periods of vigorous shaking. Hence it is very essential to design an automated seizure detection system. Electroencephalogram (EEG) signal plays vital role in measurement of these brain activities. These signals are used to diagnose seizures. Doctors can manually detect seizures with the help of these signals.In our previous work, we did comparison of performances of different machine learning classifiers like Random Forest (RF), K-Nearest Neighbour (KNN), Decision Tree (DT) and Naive Bayes (NB), and we concluded that, NB and RF algorithms give highest accuracy for classification of signals and detection of presence of seizures. In this work, we have used the reference of our previous work and developed a web application for seizure detection. We have used a voting-based ensemble model of NB and RF algorithm for classifications of EEG signals into seizures and non-seizure. An overall accuracy of 96.6% is obtained by the model." @default.
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- W3043565675 date "2020-01-01" @default.
- W3043565675 modified "2023-10-12" @default.
- W3043565675 title "WEB APPLICATION DEVELOPMENT FORSEIZURE DETECTION BASED ON ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION" @default.
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