TY  - JOUR
T1  - AUTOMATIC SLEEP STAGE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS WITH WAVELET TRANSFORM
TT  - DALGACIK DÖNÜŞÜMÜ İLE YAPAY SİNİR AĞLARI KULLANILARAK UYKU EVRELERİNİN OTOMATİK SINIFLANDIRILMASI
AU  - Öter, Ali
AU  - Aydoğan, Osman
AU  - Tuncel, Deniz
PY  - 2019
DA  - January
Y2  - 2018
DO  - 10.28948/ngumuh.516809
JF  - Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
JO  - NÖHÜ Müh. Bilim. Derg.
PB  - Nigde Omer Halisdemir University
WT  - DergiPark
SN  - 2564-6605
SP  - 59
EP  - 68
VL  - 8
IS  - 1
LA  - en
AB  - This study mainly focuses onautomatic sleep stage classification based on polysomnographic sleep recordingsobtained from obstructive sleep apnea subjects. Various studies have so farclassified sleep stages based on EEG recordings obtained from normal subjects.Because obstructive sleep apnea subjects’ sleep is often interrupted throughoutthe night, accurate scoring of their sleep disorders is important fordiagnosis. The signals for automatic sleep stages classification were selectedin accordance with American Academy of Sleep Medicine criteria. Feature vectorsconsisting of these signal power values for the automatic sleep stageclassification were calculated as inputs of ANN (Artificial Neural Networks). We re-ordered the featurevector table obtained from signals via the algorithm developed to increase thesuccess of the ANN. In this study, training and testing success of ANN weredetermined by using 10-fold cross-validation. In the study of automatic sleepstage 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 testsuccess of automatic sleep stage classification are better compared to theother studies in the literature.
KW  - Polysomnogram
KW  - Wavelet transform
KW  - Artificial Neural Networks
KW  - Sleep scoring
KW  - Sleep stages
N2  - Bu çalışmada, Tıkayıcı uyku apnesi sahipkişilerden elde edilen polisomnografik uyku kayıtlarına dayanan otomatik uykuevresi sınıflandırma çalışması yapılmıştır. Çeşitli çalışmalarda, normalkişilerden elde edilen EEG kayıtlarına dayanarak uyku evrelerisınıflandırılmıştır. Tıkayıcı uyku apneli kişilerin uykusu gece boyuncasıklıkla kesintiye uğradığından, uyku bozukluklarının doğru skorlanması tanıiçin önemlidir. Otomatik uyku evrelerinin sınıflandırılması için sinyallerAmerikan Uyku Tıbbı Akademisi kriterlerine göre seçilmiştir. Otomatik uykuevrelerinin sınıflandırması için bu sinyal gücü değerlerinden oluşan özellikvektörleri, ANN (Yapay Sinir Ağları) girdileri olarak hesaplanmıştır. YSA'nınbaşarısını artırmak için geliştirilen algoritma ile sinyallerden elde edilenözellik vektör tablosunu yeniden sıralanmıştır. Bu çalışmada, YSA'nın eğitim vetest başarısı 10 kat çapraz doğrulama kullanılarak belirlenmiştir. YSA tarafından uygulanan otomatik uyku evreskorlaması çalışmasında, Uyanıklık, REM (Hızlı Göz Hareketi), NREM1 (Hızlı gözhareki olmayan), NREM2, NREM3'ün doğru tanıma oranı sırasıyla %95, % 93, % 91,% 86 ve % 92 olarak bulunmuştur. Bulgular, otomatik uyku evresi sınıflandırmaeğitim ve test başarısının literatürdeki diğer çalışmalara göre daha iyiolduğunu göstermektedir.
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UR  - https://doi.org/10.28948/ngumuh.516809
L1  - https://dergipark.org.tr/en/download/article-file/632707
ER  -