AUTOMATIC SLEEP STAGE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS WITH WAVELET TRANSFORM

Year 2019, Volume: 8 Issue: 1, 59 - 68, 28.01.2019

Ali Öter , Osman Aydoğan Deniz Tuncel

https://doi.org/10.28948/ngumuh.516809

Cited By: 1

Abstract

   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

DALGACIK DÖNÜŞÜMÜ İLE YAPAY SİNİR AĞLARI KULLANILARAK UYKU EVRELERİNİN OTOMATİK SINIFLANDIRILMASI

Abstract

  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.

Keywords

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

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