Obstructive sleep apnea is a sleep disorder which may lead to various results. While some studies used real-time systems, there are also numerous studies which focus on diagnosing Obstructive Sleep Apnea via signals obtained by polysomnography from apnea patients who spend the night in sleep laboratory. The mean, frequency and power of signals obtained from patients are frequently used. Obstructive Sleep Apnea of 74 patients were scored in this study. A visual-scoring based algorithm and a morphological filter via Artificial Neural Networks were used in order to diagnose Obstructive Sleep Apnea. After total accuracy of scoring was calculated via both methods, it was compared with visual scoring performed by the doctor. The algorithm used in the diagnosis of obstructive sleep apnea reached an average accuracy of 88.33 %, while Artificial Neural Networks and morphological filter method reached a success of 87.28 %. Scoring success was analyzed after it was grouped based on apnea/hypopnea. It is considered that both methods enable doctors to reduce time and costs in the diagnosis of Obstructive Sleep Apnea as well as ease of use.
ÖZET: Yıllardan beri Tıkayıcı Uyku Apnesi hastalığının teşhisi için çeşitli yöntemler kullanmıştır. Çalışmaların bir kısmında gerçek zamanlı sistemler kullanılırken, bir kısmında ise özellikle hastalık tanısı için, geceyi uyku laboratuvarında geçiren apne hastalarından polisomnografi cihazıyla elde edilen işaretler yardımıyla Tıkayıcı Uyku Apnesi belirlemeye dayalı çalışmalar yaygın olarak yapılmaktadır. Yapılan çalışmada hastalardan elde edilen işaretlerinin güç değerleri kullanılmıştır. Çalışmada, İleri beslemeli Yapay Sinir Ağları ve morfolojik filtre bir arada kullanılarak Tıkayıcı Uyku Apnesi belirlenmeye dayalı bir yöntem önerilmiştir. Yapılan skorlamanın toplam doğruluğu hesaplanarak, doktor tarafından yapılan görsel skorlama ile karşılaştırılmıştır. Yalnızca Yapay Sinir Ağları kullanılarak yapılan çalışmada başarım performansı düşük kalmıştır. Morfolojik filtrelerin kullanıldığında sınıflandırma performansı önemli oranda artmıştır. Önerilen yöntemle ortalama %90,7 doğruluk oranına ulaşılmıştır. Önerilen yöntem kullanılarak Tıkayıcı uyku apnesinin belirlendiğinde, doktorların incelemeler için uzun zaman kayıplarının önüne geçeceği kullanım kolaylığı sağlayacağı düşünülmektedir. Anahtar Kelimeler: Yapay Sinir Ağı, Tıkayıcı Uyku Apnesi, Morfolojik Filtre, Polisomnografi A New Method for Obstructive sleep apnea classification using Feedforward Neural Networks and Morphological FilterABSTRACT: Various metods have been used so far for the diagnosis of Obstructive Sleep Apnea. While some studies used real-time systems, there are also numerous studies which focus on diagnosing Obstructive Sleep Apnea via signals obtained by polysomnography from apnea patients who spend the night in sleep laboratory. Pover values of signals obtained from patients were used in this study. The method proposed in this study combines Feedforward Neural Network and morphological filter in order to diagnose Obstructive Sleep Apnea. After total accuracy of scoring was calculated, it was compared with visual scoring performed by the doctor. While the classification performance is low in the study carried out via Artifical Neural Networks, its performance significantly increased when ANN and morphological filters were used together. The proposed method reached an accuracy of 90.7 percent. The proposed method reduces the time in the diagnosis of Obstructive Sleep Apnea as well as offering an easier usage.
ÖZBu 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. ABSTRACTThis 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.
The process of modernization has unfolded two basic imaginations of society: heterogeneous and homogeneous. Such an imagination considering society brought about a political structure that today we call "nation state". During the process of the collapsing of empires and replaced by nation states, ignoring different ehno-cultural and religious groups and claiming in fact they were or should be one single unity prevailed. The imagery of a homogeneous society of nation states has led to the ignoring of their differences in public regulations and the violation of human rights as a result of their judicial system. Such an idea has revealed itself in Turkey during the nation state process in a radical manner. Beginning in 20th century this imagination of a homogeneous nation state started to be questioned in concept of "multiculturalism". As a result of this, main topics that have been discussed in Turkey have become secularism, freedom of religion and demands of participation of religious groups in public space. In this article, the suitability of the paradigm of multiculturalism that has emerged in Europe with the aim of conducting the diversity of European societies in terms of finding solutions to demands of religious groups in Turkey. Turkey's ideology of secularism and the secularization process will be studied in this context. In addition to that, the content and limitation of "multiculturalism" will be studied in context of the freedom of religion in Turkey.
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