Classify Epileptic EEG Signals Using Extreme Support Vector Machine for Ictal and Muscle Artifact Detection

Baiq Siska Febriani Astuti,Anda Iviana Juniani,R Mohamad Atok,Santi Wulan Purnami,Wardah Rahmatul Islamiyah,Diah Puspito Wulandari

doi:10.18178/ijmlc.2021.11.2.1031

Baiq Siska Febriani Astuti, Anda Iviana Juniani + Show 4 more

Open Access

https://doi.org/10.18178/ijmlc.2021.11.2.1031

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Abstract

EEG signals aids in diagnosing various wave signals recorded by the activities of the brain. It also produces unavoidable artifacts, in the recording process. The purpose of this study therefore is to detect ictal and artefact signals, with the aim of reducing interpretation errors especially those related to the muscle which are quite difficult to distinguish. The data used are EEG signal recording results obtained from Rumah Sakit Universitas Airlangga. It consisted of two classes, namely ictal and muscle artefact. The signal decomposition method used is a wavelet transform, known as DWT. While the extraction feature utilized, consist of quartile, maximum, minimum, mean and standard deviation. This study also utilized the SVM with linear, polynomial, RBF and ELM (ESVM) kernels. Research results shows that the ESVM classification time is faster than the SVM and other kernels. However, the values of accuracy, sensitivity, specificity and AUC are not better.

Highlights

Epilepsy is a complex collection of brain symptoms which involves varied manifestations due to occurrence of various matters
The performances of Extreme support vector machine (ESVM) are compared with various Support Vector Machine (SVM) kernel such as Linear, Polynomial, and Radial Basis Function (RBF) on EEG signal datasets
All experiments on ESVM, SVM with kernel Linear, SVM with kernel Polynomial, and SVM with kernel RBF for detection EEG signals are carried out in MATLAB environment running in Intel CORETMi5 and 4 GB DDR3 memory

Summary

INTRODUCTION

Epilepsy is a complex collection of brain symptoms which involves varied manifestations due to occurrence of various matters. Classify Epileptic EEG Signals using Extreme Support Vector Machine for Ictal and Muscle Artifact Detection. The appearance of artefact signals during recording using EGG is quite difficult to withdraw It resembles various types of EEG signal patterns. Based on several previous studies, both techniques are used with various types of preprocessing methods to produce a good performance in solving problems related to epilepsy cases. One of such techniques is the Single Hidden Layer Feed Forward Network (SLFNs), which comprises of methods used to minimize training error and the norm of output weight within Extreme Learning Machines (ELM) [10]. This study, aims to improve the performance of the SVM method to be more effective and efficient, by combining it with the famous ELM technique with high processing speed

Wavelet Transform

Extreme Support Vector Machine

Proposed Methodologies

METHODOLOGY RESEARCH

RESULTS

CONCLUSIONS