Alkolik ve Normal EEG Sinyallerinin Zaman-Alan Tanımlayıcı Analizi Tabanlı Otomatik Sınıflandırılması

Year 2023, Volume: 35 Issue: 1, 291 - 300, 28.03.2023

Berna Arı

https://doi.org/10.35234/fumbd.1222526

Abstract

Alkolizm, beyin problemlerine ve buna bağlı bilişsel, duygusal ve davranışsal bozukluklara yol açan ciddi bir hastalıktır. Alkolizmi tespit etmek için öne çıkan kaynaklardan biri, Elektroensefalogram (EEG) sinyallerini analiz etmektir. Fakat alkolik EEG sinyallerinin sınıflandırılması, alkolik kişilerin beyin hastalıklarının tanı ve tedavisine yönelik biyomedikal araştırmalarda zorlu bir süreçtir. Bu çalışmada, alkolik EEG sinyallerinden zaman-alan tanımlayıcılarına ve topluluk öğrenmesine dayalı otomatik olarak tanımlayan yeni bir yöntem sunulmaktadır. Önerilen yöntem, tek kanallı EEG sinyallerinin dalgacık paket ayrıştırma ile farklı frekans alt bantlarına ayrılması, zaman-alan tanımlayıcıları ile öznitelik çıkarımı ve topluluk torbalama ağaçları ile sınıflandırma aşamalarından oluşmaktadır. Tek kanallı EEG veri seti ile yapılan deneysel çalışmalarda %97,50 başarım sağlanmıştır. Deneysel sonuçlar önerilen yöntemin, son teknoloji yöntemlere kıyasla daha iyi bir performansa sahip olduğunu göstermektedir. Bu yöntem alkolik bireylerin otomatik tespitinde uzmanlara yardımcı olabilecektir.

Keywords

EEG, dalgacık paket ayrıştırma, zaman-alan tanımlayıcıları, sınıflandırma.

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