DALGACIK DÖNÜŞÜMÜ İLE YAPAY SİNİR AĞLARI KULLANILARAK UYKU EVRELERİNİN OTOMATİK SINIFLANDIRILMASI

Bu çalışmada, Tıkayıcı uyku apnesi sahip kişilerden elde edilen polisomnografik uyku kayıtlarına dayanan otomatik uyku evresi sınıflandırma çalışması yapılmıştır. Çeşitli çalışmalarda, normal kişilerden elde edilen EEG kayıtlarına dayanarak uyku evreleri sınıflandırılmıştır. Tıkayıcı uyku apneli kişilerin uykusu gece boyunca sıklıkla kesintiye uğradığından, uyku bozukluklarının doğru skorlanması tanı için önemlidir. Otomatik uyku evrelerinin sınıflandırılması için sinyaller Amerikan Uyku Tıbbı Akademisi kriterlerine göre seçilmiştir. Otomatik uyku evrelerinin sınıflandırması için bu sinyal gücü değerlerinden oluşan özellik vektörleri, ANN (Yapay Sinir Ağları) girdileri olarak hesaplanmıştır. YSA'nın başarısını artırmak için geliştirilen algoritma ile sinyallerden elde edilen özellik vektör tablosunu yeniden sıralanmıştır. Bu çalışmada, YSA'nın eğitim ve test başarısı 10 kat çapraz doğrulama kullanılarak belirlenmiştir. YSA tarafından uygulanan otomatik uyku evre skorlaması çalışmasında, Uyanıklık, REM (Hızlı Göz Hareketi), NREM1 (Hızlı göz hareki olmayan), NREM2, NREM3'ün doğru tanıma oranı sırasıyla %95, % 93, % 91, % 86 ve % 92 olarak bulunmuştur. Bulgular, otomatik uyku evresi sınıflandırma eğitim ve test başarısının literatürdeki diğer çalışmalara göre daha iyi olduğunu göstermektedir.

Anahtar Kelimeler:

Polisomnografi, Dalgacık Dönüşümü, Yapay Sinir Ağları, Uyku skorlama, Uyku Evreleri

AUTOMATIC SLEEP STAGE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS WITH WAVELET TRANSFORM

This study mainly focuses on automatic sleep stage classification based on polysomnographic sleep recordings obtained from obstructive sleep apnea subjects. Various studies have so far classified sleep stages based on EEG recordings obtained from normal subjects. Because obstructive sleep apnea subjects’ sleep is often interrupted throughout the night, accurate scoring of their sleep disorders is important for diagnosis. The signals for automatic sleep stages classification were selected in accordance with American Academy of Sleep Medicine criteria. Feature vectors consisting of these signal power values for the automatic sleep stage classification were calculated as inputs of ANN (Artificial Neural Networks). We re-ordered the feature vector table obtained from signals via the algorithm developed to increase the success of the ANN. In this study, training and testing success of ANN were determined by using 10-fold cross-validation. In the study of automatic sleep stage scoring implemented by ANN, the correct recognition rate of Wakefulness, REM (Rapid Eye Movement), NREM1(Non REM1), NREM2, NREM3 were found as 95%, 93%, 91%, 86% and 92%, respectively. The findings suggest that training and test success of automatic sleep stage classification are better compared to the other studies in the literature.

Keywords:

Polysomnogram, Wavelet transform, Artificial Neural Networks, Sleep scoring, Sleep stages,

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