Detection of REM in sleep EOG signals
Küçük Resim Yok
Tarih
2016
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Indian Society for Education and Environment
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Background/Objectives: Sleep staging is very important phase for diagnosing respiration and sleep diseases. Nowadays, Electroencephalogram (EEG), Electromyogram (EMG), Electrooculogram (EOG) signals are particularly used together in studies on sleep staging. Methods/Statistical Analysis: Associating only EOG signals to sleep staging was distinctly purposed. So, this paper deals with extraction features and classifying for determining REM-NREM states from the EOG signals. In this study, left eye (LEOG) and the right eye (REOG) signals were used. After EOG signals were obtained, 21 different features were extracted from LEOG and REOG in time and frequency domain according to rules of American Academy of Sleep Medicine (AASM). Findings: Artificial Neural Networks (ANN) was adopted on features as method of classification with 3-fold cross validation technique and reached conclusion with the maximum test classification accuracy as 88.05%. To obtain higher classification accuracies, Sequential Backward Selection (SBS) method was used. According to results of SBS, number of the best features combination was determined as 13 and the maximum classification accuracy was obtained as 89.62%. The optimum value of hidden layer node number of ANN was determined as 15 for the best features. Application/Improvements: When looking from the viewpoint of percentage of classification accuracy of this study, a result can be seen that is non-negligible value for literature.
Açıklama
Anahtar Kelimeler
Artificial neural networks, EOG, Feature selection, SBS, Sleep stage
Kaynak
Indian Journal of Science and Technology
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
9
Sayı
25
