Increasing accuracy of the epilepsy signal classification

Hindarto, Hindarto (2021) Increasing accuracy of the epilepsy signal classification. IOP Conference Series: Materials Science and Engineering.

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Abstract

Epilepsy is a condition that can cause a person to experience seizures repeatedly. Epilepsy can attack someone when there is damage or changes in the brain. This study researchers tried to use sampling techniques as a feature of extracting epilepsy signal features and the K-NN method to identify epilepsy signal patterns. The data of this study took epilepsy signal data from the University of Bonn's Epileptologie clinic which consisted of data set A, open eye normal signal, set B normal closed eye signal, set C in epilepsy zone, set D enter epilepsy, set E seizure epilepsy. In this study, researchers tried to classify data set A, data for normal people and data set E, data for people who have epilepsy. Data set A consists of 100 EEG signals and data set E consists of 100 EEG signal data. The data used are data for the training process as much as 50 Epilepsy signal data and data for the trial process as many as 50 Epilepsy signal data. In the trial process the classification results reach 100% accuracy. The trial process uses the value of K = 1 to the value of K = 9

Item Type:	Other
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering > School of Computer Engineering
Depositing User:	mr hindarto hindarto
Date Deposited:	30 Mar 2021 07:51
Last Modified:	14 Apr 2021 02:05
URI:	http://eprints.umsida.ac.id/id/eprint/8383

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