Göksu Zekiye ÖZEN, Rayımbek SULTANOV, YUNUS ÖZEN, Zahide YILMAZ GÜNEŞ

A Convolutional Neural Network Using Raw EEG Signal Obtained from Single Channel for Automatic Sleep Staging

Sleep stages are determined firstly for the evaluation of sleep quality and the diagnosis of sleep diseases. The signals, recorded from sensors connected to various parts of the body, such as electroencephalogram (EEG), electrocardiogram (ECG), electrooculogram (EOG) and electromyogram (EMG) are used for this purpose. After the production of affordable wearable EEG devices for individual use, studies have begun to detect sleep stages from a single channel EEG signal. This paper presents an automated system that can perform sleep staging using a single-channel raw EEG signal. A Convolutional Neural Network (CNN) model was trained with the raw EEG signal for sleep stage detection. The use of CNN does not require any feature extraction. The developed CNN model classifies the sleep data sampled at 250 Hz, divided into 30-second segments according to the 5-class sleep staging system. According to the test results, the performance of the proposed system was found to be 93% macro F1 score and 92% accuracy.

Tek Kanallı Ham EEG Sinyali Temelli Otomatik Uyku Evrelemesi Yapan Evrişimsel Sinir Ağı

Uyku kalitesinin değerlendirilmesi ve uyku hastalıklarının teşhisi için öncelikle uyku evreleri tespit edilmektedir. Bunun için vücudun çeşitli bölgelerine bağlı sensörlerden kaydedilen elektroensefalogram (EEG), elektrokardiyogram (ECG), elektrookülogram (EOG), elektromiyogram (EMG) gibi sinyaller kullanılmaktadır. Bireysel kullanım için uygun fiyatlı giyilebilir EEG cihazlarının üretilmesi ile tek kanallı EEG sinyalinden uyku evreleri tespiti yapılabilmesi için çalışmalar başlamıştır. Bu makalede tek kanallı ham EEG sinyali kullanarak uyku evreleri tespiti yapabilen otomatik bir sistem sunulmaktadır. Bu amaçla ham EEG sinyalleri ile bir Evrişimsel Sinir Ağı (ESA) modeli eğitilmiştir. ESA kullanımı sayesinde herhangi bir özellik çıkarımı yapılmasına ihtiyaç bulunmamaktadır. Geliştirilen ESA modeli 250 Hz’de örneklenmiş, 30 sn’lik segmentlere bölünmüş uyku verisini 5 sınıflı uyku evrelemesi sistemine göre sınıflandırmaktadır. Yapılan testlerin sonuçlarına göre önerilen sistemin başarımı %93 makro F1-skoru ve %92 doğruluk olarak bulunmuştur.

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