论文信息 - Detection of REM in Sleep EOG Signals

Detection of REM in Sleep EOG Signals

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.

[1] Kemal Polat,et al. Examining the relevance with sleep stages of time domain features of EEG, EOG, and chin EMG signals , 2009, 2009 14th National Biomedical Engineering Meeting.

[2] Chusak Limsakul,et al. Evaluating Feature Extraction Methods of Electrooculography (EOG) Signal for Human-Computer Interface , 2012 .

[3] Daniel J Buysse,et al. Sleep–Related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research , 2000 .

[4] Sheng-Fu Liang,et al. A rule-based automatic sleep staging method , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[6] Deniz Tuncel,et al. Automatic classification of sleep stages with artificial neural networks according to visual scoring rules , 2015, 2015 23nd Signal Processing and Communications Applications Conference (SIU).

[7] Dean Cvetkovic,et al. Automatic sleep-wake detection using electrooculogram signals , 2014, 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES).