Migren Tanısında Öncü Sinyal Ayrıştırma Yöntemlerinin Performanslarının Karşılaştırılması

Year 2022, Volume: 13 Issue: 3, 413 - 419, 30.09.2022

Zülfikar Aslan

https://doi.org/10.24012/dumf.1103391

Abstract

Bu çalışma, migren hastalığını (MH) Elektroensefalogram (EEG) verisi kullanarak otomatik olarak teşhisini gerçekleştirmek amacıyla bir bilgisayar destekli tanı sistemi sunmaktadır. Ayrıca önerilen yöntemin farklı ayrıştırma yöntemleri ile test edilerek karşılaştırmalı analizi gerçekleştirilmiştir. EEG sinyalleri Çok Ölçekli Temel Bileşen Analizi (MSPCA) uygulanarak var olan gürültüler giderildikten sonra, Ayarlanabilir Q Faktör Dalgacık Dönüşümü (TQWT), Ampirik Mod Ayrıştırma (EMD) ve Ampirik Dalgacık Dönüşümü (EWT) ayrıştırma yöntemleri ile içsel mod fonksiyonları (IMF) bileşenlerine ayrılmıştır. Her bir IMF bileşeninden istatistiksel özellikler hesaplanarak özellik çıkarım işlemi gerçekleştirilmiştir. Sınıflandırma performansları, her bir IMF bileşeninin özellikleri, Rastgele Orman algoritması ile sınıflandırılarak test edilmiştir. En yüksek sınıflandırma doğruluğu IMF1 ve IMF2 bileşenlerinden elde edilmiştir. IMF1 bileşenine ait özelliklerin Rastgele Orman algoritması ile sınıflandırılmasıyla TQWT için 88.9%, EMD için 92.47% ve EWT için 81.41% sınıflandırma başarımı elde edilmiştir. Bu çalışmada gerçekleştirilen deneysel çalışmalar da EMD ayrıştırma yöntemi karşılaştırılan diğer yöntemlere göre MH ve sağlıklı kontrol deneklerin ayırt edilmesinde iyi bir performans sergilediği gözlemlenmiştir.

Keywords

Migren Teşhisi , Elektroensefalografi , Ayrıştırma Yöntemleri , Ampirik Mod Ayrıştırması , Ayarlanabilir Q Faktörü Dalgacık Dönüşümü , Ampirik Dalgacık Dönüşümü

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