Uyku evrelerinin sınıflandırılmasında EEG ve EOG sinyallerinin karşılaştırılması

Yaşamın en önemli parçası olan uykunun değeri, uykusuzluğun neden olduğu sağlık sorunlarının ortaya çıkmasıyla birlikte artmaktadır. Bu sorunu çözmek için uyku evrelerinde ortaya çıkan farklı sinyal kalıplarını yorumlamak son derece önemlidir. Bu amaca ulaşmak için uyku evrelerinin otomatik olarak puanlanmasını sağlayan sistemler oluşturulur. Uyku puanlamasında uyuyan kişinin elektrofizyolojik sinyalleri dikkate alınarak uyku hakkında değerli bilgiler elde edilir. Çalışmada uyku alanında çalışan araştırmacılara açık erişim olarak sunulan ISRUC-Sleep veri seti kullanılmıştır. Çalışmanın temel amacı, uyku evrelerinin sınıflandırılmasında elektroensefalografi (EEG) ve elektrookülografi (EOG) biyosinyallerinin etkisini araştırmaktır. Analiz, ISRUC platformuna ait üç farklı grubu tanımlayan veri setinin üçüncü grubu dikkate alınarak gerçekleştirilmiştir. Veri setindeki alt grup_3'ün 10 katılımcısı dikkate alınmıştır. Etkili öznitelikler çıkarılarak ve farklı sınıflandırma yöntemleri uygulanarak aşamaların sınıflandırılmasında EEG veya EOG sinyallerinden hangisinin daha iyi olduğu araştırılmıştır. Kullanılan sınıflandırma yöntemlerinin performans değerlendirmesi açısından önceki çalışmamızda sunulan yeni Roza metriği uygulanmıştır. Welch öznitelik çıkarma yöntemi ve toplu ağaç sınıflandırma tekniği sayesinde uyku evrelerinin sınıflandırılmasında EEG sinyallerinin EOG'dan daha başarılı olduğu kanıtlanmıştır. Bu uyku evreleri EEG sinyallerini kullanarak %77.7 başarı oranıyla sınıflandırılmıştır.

Anahtar Kelimeler:

Uyku, EEG, EOG, Öznitelik çıkarma, Sınıflandırma, Roza

Comparison of EEG and EOG signals in classification of sleep stages

The value of sleep, which is the most significant part of life, increases with the emergence of health problems caused by insomnia. To solve this problem, it is extremely important to interpret the different signal patterns that occur during sleep stages. In order to achieve this goal, systems are created that provide automatic scoring of sleep stages. In sleep scoring, valuable information about sleep is obtained by considering the electrophysiological signals of the sleeper. The ISRUCSleep dataset, which was presented as open access to researchers working in the field of sleep, was used in the study. The main goal of the study is to investigate the effect of electroencephalography (EEG) and electrooculography (EOG) biosignals in the classification of sleep stages. The analysis was carried out by considering the third group of the data set, which defines three different groups belonging to the ISRUC platform. The 10 participants of subgrup_3 in the dataset were considered. By extracting effective features and applying different classification methods, it was investigated which one of the EEG or EOG signals was better in the classification of stages. In terms of performance evaluation of the classification methods used, the new Roza metric presented in our previous study was applied. It has been proven that EEG signals are more successful than EOG in the classification of sleep stages, thanks to the Welch feature extraction method and the ensemble of bagged tree classification technique. These sleep stages were classified by using EEG signals with a success rate of 77.7%.

Keywords:

Sleep, EEG, EOG, Feature extraction, Classification, Roza,

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