2 Boyutlu Evrişimsel Sinir Ağları ile EEG Tabanlı Otomatik Uyku Evrelerinin Sınıflandırılması

Uyku bozuklukları toplumda oldukça yaygın görülmekle birlikte çeşitli sağlık sorunlarına neden olmaktadır. Bu bozuklukların teşhis edilmesi ve uyku kalitesinin belirlenmesi için Polisomnogram metodu ile birçok fizyolojik veri toplanılır. En önemli data uyku halinde beyinden kaydedilen EEG verisidir. Saatler süren uykuya ait EEG verilerinin uzmanlar tarafından analiz edilmesi yüksek dikkat isteyen çok zahmetli bir iştir. Son zamanlarda insan hatalarını önlemek ve hızlı nesnel bir analiz gerçekleştirmek amacıyla EEG sinyallerini kullanan otomatik uyku evre sınıflandırıları geliştirilmiştir. Bu sınıflandırıcılar makine öğrenmesi yöntemlerini kullanır ve her bir EEG kesitine dair uyku evresini tahmin eder. Geleneksel makine öğrenmesi yöntemlerine kıyasla elle hiçbir öznitelik çıkarımı gerektirmeyen derin öğrenme uyku evre sınıflandırmasında daha başarılı olabilmiştir. Son zamanlarda, tek boyutlu evrişimsel sinir ağları otomatik uyku evre sınıflandırmasında ana yöntem olmuştur. Bu araştırmada iki boyutlu basit bir evrişimsel sinir ağlarına dayalı otomatik uyku evre sınıflandırılmasının uygulanabilirliği incelenmiştir. iki boyutlu evrişimsel sinir ağlarının %92.5 doğruluk ve 0.82 Cohen Kappa değeri ile sınıflandırmabildiği bulunmuştur.

Anahtar Kelimeler:

EEG, Otomatik Uyku Evre sınıflandırma, Derin öğrenme, Evrişimsel Sinir Ağları

EEG Based Automatic Sleep Staging via Simple 2D-Convolutional Neural Network

Sleep disorders have high prevalence and cause various health problems. For the diagnostics of these disorders and assessment of the sleep quality, many physiological data are collected using polysomnogram (PSG) method. The most important PSG data is the EEG recorded from the brain during sleep. Analysis of hours of sleep EEG data by experts is an onerous task which requires high attention. Recently, many automatic sleep staging classifiers using EEG are developed in order to prevent human error, and to provide a quick objective analysis. They use machine learning techniques and predict the sleep stage of each EEG epoch. Compared to traditional machine learning, deep learning which requires no hand-crafted feature extraction was able to classify sleep stages better. 1D Convolutional Neural Networks (CNN) are the main methods used in automatic sleep staging recently. In this research a simple 2D-CNN based automatic sleep staging feasibility is investigated. It has been found that a 2D CNN can classify the sleep stages by accuracy of 92.55% and with a Cohen’s kappa of 0.82.

Keywords:

Automatic Sleep Staging, EEG, Deep Learning, Convolutional Neural Network (CNN).,

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