Library Automation and Digital Archive
Fakultas Ilmu Komputer
Universitas Indonesia

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Call Number SEM-372
Collection Type Indeks Artikel prosiding/Sem
Title A Comparitive Study of Machine Learning Algorithms for Epileptic Seizure Classification on EEG Signals. Hal 401-408
Author Elly Matul Imah, Arif Widodo;
Publisher ICACSIS 2017 International Conference on Advanced Computer Science and Information System
Subject EEG, epilepsy seizure, GRLVQ, SVM, Random Forest, Backpropogation, PCA, Wavelet
Lokasi : Perpustakaan Fakultas Ilmu Komputer
Nomor Panggil ID Koleksi Status
Tidak ada review pada koleksi ini: 47388
Abstract- Electroencephalography (EEG) is a tool for monitoring brain activity which is important for identifying epilepsy seizure. Automatic epileptic seizure identification in EEG is a challenging task and useful for helping neurophysiologist. This study Compares some algorithms in machine learning algorithms for epilepsy seizure identification based on EEG data. The classification algorithms compared in this (GRLVQ), Backpropagation, SVM, and random forest, combined with wavelet and PCA feature extraction. The EEG signals used in this study were obtained from EEG dataset which was developed by University of Bonn. EEG epilepsy seizure dataset has five classes. Class A and B are From five healthy subjects, where C and D are from five healthy subjects in open and closed eyes. Class C,D and E from five elliptic subjects, where C and D are no-seizure signals, and E contains only seizure signal. The tasks that are used to compare the performance of feature extraction and classification algorithm is classifying 5 classes of EEG epilepsy seizure on EEG accuracy, recall, precision training and testing times. The best performance in recognizing the five classes in EEG epileptic seizure datasets is GRLVQ, with the accuracy, precision and recall is 0.9866 and testing time is less than 0.1 seconds.