Epilepsi Tespitinde Gürbüz Yerel Ortalama Ayrışım ve Ampirik Kip Ayrışım Yöntemlerinin Performans Analizi

Year 2022, Issue: 39, 132 - 137, 31.07.2022

Oğuzkaan Çatalkaya , Tuba Hazman , Sabrına Turturova , Tuğba Şentürk , Fatma Latifoğlu

https://doi.org/10.31590/ejosat.1145969

Abstract

Beynin elektriksel aktivitesi ile ilgili bilgi sağlayan elektroansefalografi (EEG) verileri nörolojik hastalıkların tanısında yaygın olarak kullanılmaktadır. Yaklaşık olarak dünya nüfusunun %1'ini etkileyen hastalıklardan biri olan epilepsi tespitinde de EEG sinyalleri önemli bilgiler sağlamaktadır. Bu çalışmada EEG sinyalleri kullanılarak epilepsi nöbetinin nöbet öncesi tespiti amaçlanmıştır. Bu amaç doğrultusunda epilepsi ve sağlıklı bireylerden alınan farklı durumlardaki EEG sinyalleri kullanılarak ön işleme adımları gerçekleştirildikten sonra EEG sinyallerinden, Gürbüz Yerel Ortalama Ayrışım (Robust Local Mean Decomposition, RLMD) ve Ampirik Kip Ayrışım (AKA) yöntemi kullanılarak elde edilen alt bant sinyallerinden öznitelikler çıkarılmıştır. Elde edilen öznitelikler ve Yapay Sinir ağları (YSA) ile sınıflandırma çalışmaları yapılmıştır. Yapılan çalışmalar doğrultusunda EEG sinyallerinin farklı durumlarına ait sınıflandırma sonuçları doğruluk, duyarlılık, özgüllük, kesinlik ve f1 skoru performans parametreleri kullanılarak ortaya konmuştur.

Keywords

Epilepsi, EEG, Gürbüz Yerel Ortalama Ayrışım Yöntemi, Ampirik Kip Ayrışım Yöntemi, Yapay Sinir Ağları

Supporting Institution

TÜBİTAK

Project Number

1919B012111299

Thanks

Bu çalışma Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı kapsamında 1919B012111299 numaralı proje ile desteklenmiştir. Bu sebeple TÜBİTAK 'a teşekkürlerimizi sunarız.

Performance Analysis of Robust Local Mean Decomposition and Empirical Mode Decomposition Methods in the Detection of Epilepsy

Abstract

Electroencephalography (EEG) data, which provides information about the electrical activity of the brain, are widely used in the diagnosis of neurological diseases, EEG signals also provide important information in the detection of epilepsy, which is one of the diseases affecting approximately 1% of the world’s population. In this study, it was aimed to detect the epileptic seizure before the seizure by using EEG signals. For this purpose, after preprocessing steps were performed by using EEG signals in different situations from epilepsy and healthy individuals, features were extracted from EEG signals from subband signals obtained by using Robust Local Mean Decomposition (RLMD) and Empirical Mode Decomposition (AKA) methods. Classification studies were carried out with the obtained features and Artificial Neural Networks (ANN). In line with the studies, the classification results of the different states of the EEG signals were revealed using the performance parameters of accuracy, sensitivity, specificity, precision and f1 score.

Keywords

Epilepsy, EEG, Robust Local Mean Decomposition, Empirical Mode Decomposition Method, Artificial Neural Networks

Project Number

1919B012111299

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