EEG İşaretlerinin Hilbert Huang Dönüşümü ve Sınıflandırılması

Year 2022, , 1323 - 1333, 28.12.2022

Gazi Akgün , Ömer Akgün

https://doi.org/10.35414/akufemubid.1145857

Abstract

Bu çalışmada Elektroensefalogram (EEG) sinyallerinin analizi ve bu analiz üzerinden sınıflandırılması amaçlanmıştır. Bu amaçla EEG işaretleri Hilbert Huang metodu ile alt frekans bantlarındaki bileşenlerine ayrılmış, anlık frekans ve marjinal izge vektörleri elde edilmiştir. Bu vektörler ve bileşenler kullanılarak istatistiksel öznitelikleri çıkarılmıştır. Bu öznitelikler göz açık – göz kapalı , sağlıklı-epileptik ve epileptik nöbet alt sınıflarında incelenmiş, destek vektör makinesi (DVM), yapay sinir ağları (YSA) ve doğrusal ayrım analizi (DAA) algoritmaları ile sınıflandırılmış ve sonuçlar karşılaştırmalı olarak tartışılmıştır.

Keywords

EEG, Epilepsi, Hilbert Huang, Ampirik Mod Ayrışımı, Yapay Sinir Ağları, Destek Vektör Makineleri, Sınıflandırma

Hilbert Huang Transformation and Classification of EEG Signals

Abstract

The goal of this study is to classify the Electroencephalogram (EEG) signals through signal analysis. To achieve this, Hilbert Huang's method is used to decompose EEG signals into components in lower frequency bands, yielding instantaneous frequency and marginal spectral vectors. These vectors and components are then used to extract statistical features. These features are classified in the eye-open, eye-closed, healthy-epileptic, and epileptic seizure subclasses with the support vector machine (SVM), artificial neural networks (ANN), and linear discrimination analysis (LDA) algorithms, and the results are discussed in comparison.

Keywords

EEG, Epilepsy, Hilbert Huang, Emprical Mode Decomposition, Artificial Neural Networks, Support Vector Machines, Classification

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