Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)

Epilepsy is a disease that attacks the brain and results in seizures due to neurological disorders. The electrical activity of the brain recorded by the EEG signal test, because EEG test can be used to diagnose brain and mental diseases such as epilepsy. This study aims to identify whether a person...

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Autores principales:	Suwanto Suwanto, M. Hasan Bisri, Dian Candra Rini Novitasari, Ahmad Hanif Asyhar
Formato:	article
Lenguaje:	EN
Publicado:	Department of Mathematics, UIN Sunan Ampel Surabaya 2019
Materias:	Epilepsy; EEG; Feature Extraction; Classification Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/a53d6841c03b4d499cf57b5fcd11403f
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spelling	oai:doaj.org-article:a53d6841c03b4d499cf57b5fcd11403f2021-12-02T14:27:00ZClassification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)2527-31592527-316710.15642/mantik.2019.5.1.35-44https://doaj.org/article/a53d6841c03b4d499cf57b5fcd11403f2019-05-01T00:00:00Zhttp://jurnalsaintek.uinsby.ac.id/index.php/mantik/article/view/538https://doaj.org/toc/2527-3159https://doaj.org/toc/2527-3167Epilepsy is a disease that attacks the brain and results in seizures due to neurological disorders. The electrical activity of the brain recorded by the EEG signal test, because EEG test can be used to diagnose brain and mental diseases such as epilepsy. This study aims to identify whether a person has epilepsy or not along with the result of accurate, sensitivity, and precision rate using Fast Fourier Transform (FFT) and Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The FFT is used to transform EEG signals from time-based into frequency-based and continued with feature extraction to take characteristics from each filtering signal using the median, mean, and standard deviations of each EEG signal. The results of the feature extraction used for input on the category process based on characteristics data (classification) using ANFIS. EEG signal data is obtained from epilepsy center online database of Bonn University, German. The results of the EEG signal classification system using ANFIS with two classes (Normal-Epilepsy) states accuracy, sensitivity, and precision of 100%. The classification systems with three class division (Normal-Not Seizure Epilepsy-Epilepsy) resulted in an accuracy of 89.33% sensitivity of 89.37% and precision of 89.33%.Suwanto SuwantoM. Hasan BisriDian Candra Rini NovitasariAhmad Hanif AsyharDepartment of Mathematics, UIN Sunan Ampel SurabayaarticleEpilepsy; EEG; Feature Extraction; ClassificationMathematicsQA1-939ENMantik: Jurnal Matematika, Vol 5, Iss 1, Pp 35-44 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Epilepsy; EEG; Feature Extraction; Classification Mathematics QA1-939
spellingShingle	Epilepsy; EEG; Feature Extraction; Classification Mathematics QA1-939 Suwanto Suwanto M. Hasan Bisri Dian Candra Rini Novitasari Ahmad Hanif Asyhar Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)
description	Epilepsy is a disease that attacks the brain and results in seizures due to neurological disorders. The electrical activity of the brain recorded by the EEG signal test, because EEG test can be used to diagnose brain and mental diseases such as epilepsy. This study aims to identify whether a person has epilepsy or not along with the result of accurate, sensitivity, and precision rate using Fast Fourier Transform (FFT) and Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The FFT is used to transform EEG signals from time-based into frequency-based and continued with feature extraction to take characteristics from each filtering signal using the median, mean, and standard deviations of each EEG signal. The results of the feature extraction used for input on the category process based on characteristics data (classification) using ANFIS. EEG signal data is obtained from epilepsy center online database of Bonn University, German. The results of the EEG signal classification system using ANFIS with two classes (Normal-Epilepsy) states accuracy, sensitivity, and precision of 100%. The classification systems with three class division (Normal-Not Seizure Epilepsy-Epilepsy) resulted in an accuracy of 89.33% sensitivity of 89.37% and precision of 89.33%.
format	article
author	Suwanto Suwanto M. Hasan Bisri Dian Candra Rini Novitasari Ahmad Hanif Asyhar
author_facet	Suwanto Suwanto M. Hasan Bisri Dian Candra Rini Novitasari Ahmad Hanif Asyhar
author_sort	Suwanto Suwanto
title	Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)
title_short	Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)
title_full	Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)
title_fullStr	Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)
title_full_unstemmed	Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)
title_sort	classification of eeg signals using fast fourier transform (fft) and adaptive neuro fuzzy inference system (anfis)
publisher	Department of Mathematics, UIN Sunan Ampel Surabaya
publishDate	2019
url	https://doaj.org/article/a53d6841c03b4d499cf57b5fcd11403f
work_keys_str_mv	AT suwantosuwanto classificationofeegsignalsusingfastfouriertransformfftandadaptiveneurofuzzyinferencesystemanfis AT mhasanbisri classificationofeegsignalsusingfastfouriertransformfftandadaptiveneurofuzzyinferencesystemanfis AT diancandrarininovitasari classificationofeegsignalsusingfastfouriertransformfftandadaptiveneurofuzzyinferencesystemanfis AT ahmadhanifasyhar classificationofeegsignalsusingfastfouriertransformfftandadaptiveneurofuzzyinferencesystemanfis
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Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)

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