ISSN 2394-5125
 

Research Article 


WEB APPLICATION DEVELOPMENT FORSEIZURE DETECTION BASED ON ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION

Pranita Garde, Anuradha Thakare, Aishwarya Biradar, Neha Pawar.

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 Nave 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.

Key words: Electroencephalogram (EEG) signals, ensemble learning, seizure


 
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How to Cite this Article
Pubmed Style

Pranita Garde, Anuradha Thakare, Aishwarya Biradar, Neha Pawar. WEB APPLICATION DEVELOPMENT FORSEIZURE DETECTION BASED ON ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION. JCR. 2020; 7(19): 1228-1236. doi:10.31838/jcr.07.19.150


Web Style

Pranita Garde, Anuradha Thakare, Aishwarya Biradar, Neha Pawar. WEB APPLICATION DEVELOPMENT FORSEIZURE DETECTION BASED ON ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION. http://www.jcreview.com/?mno=104132 [Access: July 17, 2020]. doi:10.31838/jcr.07.19.150


AMA (American Medical Association) Style

Pranita Garde, Anuradha Thakare, Aishwarya Biradar, Neha Pawar. WEB APPLICATION DEVELOPMENT FORSEIZURE DETECTION BASED ON ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION. JCR. 2020; 7(19): 1228-1236. doi:10.31838/jcr.07.19.150



Vancouver/ICMJE Style

Pranita Garde, Anuradha Thakare, Aishwarya Biradar, Neha Pawar. WEB APPLICATION DEVELOPMENT FORSEIZURE DETECTION BASED ON ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION. JCR. (2020), [cited July 17, 2020]; 7(19): 1228-1236. doi:10.31838/jcr.07.19.150



Harvard Style

Pranita Garde, Anuradha Thakare, Aishwarya Biradar, Neha Pawar (2020) WEB APPLICATION DEVELOPMENT FORSEIZURE DETECTION BASED ON ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION. JCR, 7 (19), 1228-1236. doi:10.31838/jcr.07.19.150



Turabian Style

Pranita Garde, Anuradha Thakare, Aishwarya Biradar, Neha Pawar. 2020. WEB APPLICATION DEVELOPMENT FORSEIZURE DETECTION BASED ON ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION. Journal of Critical Reviews, 7 (19), 1228-1236. doi:10.31838/jcr.07.19.150



Chicago Style

Pranita Garde, Anuradha Thakare, Aishwarya Biradar, Neha Pawar. "WEB APPLICATION DEVELOPMENT FORSEIZURE DETECTION BASED ON ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION." Journal of Critical Reviews 7 (2020), 1228-1236. doi:10.31838/jcr.07.19.150



MLA (The Modern Language Association) Style

Pranita Garde, Anuradha Thakare, Aishwarya Biradar, Neha Pawar. "WEB APPLICATION DEVELOPMENT FORSEIZURE DETECTION BASED ON ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION." Journal of Critical Reviews 7.19 (2020), 1228-1236. Print. doi:10.31838/jcr.07.19.150



APA (American Psychological Association) Style

Pranita Garde, Anuradha Thakare, Aishwarya Biradar, Neha Pawar (2020) WEB APPLICATION DEVELOPMENT FORSEIZURE DETECTION BASED ON ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION. Journal of Critical Reviews, 7 (19), 1228-1236. doi:10.31838/jcr.07.19.150





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