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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Classification of Sleep Stages via Machine Learning Algorithms

Makine Öğrenmesi Algoritmaları ile Uyku Evrelerinin Sınıflandırılması

How to cite: Bulut A, Öztürk G, Kaya . Classification of sleep stages via machine learning algorithms. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2022; 5(1): 66-70. DOI: 10.54856/jiswa.202205210

Full Text: PDF, in English.

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Title: Classification of Sleep Stages via Machine Learning Algorithms

Abstract: Sleep is a natural form of rest for humans. People need sleep to perform their daily functions. Insufficient or unstable sleep may adversely affect the function of many systems in human body. Sleep disorders can be seen common and cause serious health problems that affect quality of life. From past to present, it has become imperative to classify sleep stages in order to accurately analyze and diagnose these disorders. This classification is made by people who are experts in the field of sleep. However, this process is a very laborious task that requires high attention, and since it is done by a human, it is quite normal to make wrong classifications. As a solution to this, it is possible to make these classifications with machine learning techniques to obtain more accurate results. In this study, we compared different classification methods with each other and examined the channel-based accuracy of the method that gives the highest accuracy based on channels. The accuracy of the Fine Gaussian SVM Method was 98.9% and the F1-score was 98.95, the accuracy of the Weighted KNN Method was 97.9% and the F1-score was 97.89, the accuracy of the Wide Neural Network Method was 97.4% and the F1-score was 97.09, the accuracy of the Cubic SVM Method was 96.2% and the F1-score was 96.36. When we examine the Fine Gaussian SVM Method with the highest accuracy based on channels, we found accuracy of only Fpz-CZ channel is 98.1%, accuracy of only Pz-Oz channel is 94.5%.

Keywords: Sleep; Sleep stages; Machine learning; Automatic sleep staging


Başlık: Makine Öğrenmesi Algoritmaları ile Uyku Evrelerinin Sınıflandırılması

Özet: Uyku, insanlar için doğal bir dinlenme halidir. İnsanlar günlük işlevlerini yerine getirebilmek için uykuya ihtiyaç duyarlar. Yetersiz veya dengesiz uyku, insan vücudundaki birçok sistemin işlevini olumsuz etkileyebilir. Uyku bozuklukları yaygın olarak görülebilmekte ve yaşam kalitelerini etkileyen ciddi sağlık sorunlarına neden olmaktadır. Geçmişten günümüze bu bozuklukların doğru bir şekilde analiz ve teşhis edilebilmesi için uyku evrelerinin sınıflandırılması zorunlu hale gelmiştir. Bu sınıflandırma uyku alanında uzman kişiler tarafından yapılmaktadır. Ancak bu işlem oldukça zahmetli ve yüksek dikkat gerektiren bir iştir ve bir insan tarafından yapıldığından yanlış sınıflandırmalar yapılması oldukça normaldir. Buna çözüm olarak daha doğru sonuçlar elde etmek için bu sınıflandırmaları makine öğrenmesi teknikleri ile yapmak mümkündür. Biz bu çalışmada farklı sınıflandırma metodlarını birbiriyle kıyasladık ve en fazla doğruluk veren metodu kanal bazlı inceledik. Fine Gaussian SVM Metodu için doğruluğu 98.9% ve F1-skoru 98.95, Weighted KNN Metodu için doğruluğu 97.9% ve F1- skoru 97.89, Wide Neural Network Methodu için doğruluğu 97.4% ve F1- skoru 97.09, Cubic SVM Metodu için doğruluğu 96.2% ve F1- skoru 96.36 olarak bulduk. En yüksek başarı oranına sahip Fine Gaussian SVM Metodunu kanal bazlı incelediğimizde ise sadece Fpz-CZ kanalının kullanılmasıyla doğruluğu 98.1%, sadece Pz-Oz kanalının kullanılmasıyla doğruluğu 94.5% bulduk.

Anahtar kelimeler: Uyku; Uyku evreleri; Makine öğrenmesi; Otomatik uyku evresi sınıflandırma


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