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Sun, M., Ryan, N.D., Dahl, R.E., Hsin, H.C., lyengar, S. and Sclabassi, R.J. (1993) A Neural Network System for Automatic Classification of Sleep Stages. Proceedings of the 12th Southern Biomedical Engineering Conference, 137-139.

has been cited by the following article:

• TITLE: A Sleep Scoring System Using EEG Combined Spectral and Detrended Fluctuation Analysis Features

  AUTHORS: Amr F. Farag, Shereen M. El-Metwally, Ahmed A. Morsy

  KEYWORDS: Automated Sleep Staging, Detrended Fluctuation Analysis (DFA), Decision Tree, Multi-Layer Perceptron (MLP)

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.7 No.8, June 26, 2014

  ABSTRACT: Most of sleep disorders are diagnosed based on the sleep scoring and assessments. The purpose of this study is to combine detrended fluctuation analysis features and spectral features of single electroencephalograph (EEG) channel for the purpose of building an automated sleep staging system based on the hybrid prediction engine model. The testing results of the model were promising as the classification accuracies were 98.85%, 92.26%, 94.4%, 95.16% and 93.68% for the wake, non-rapid eye movement S1, non-rapid eye movement S2, non-rapid eye movement S3 and rapid eye movement sleep stages, respectively. The overall classification accuracy was 85.18%. We concluded that it might be possible to employ this approach to build an industrial sleep assessment system that reduced the number of channels that affected the sleep quality and the effort excreted by sleep specialists through the process of the sleep scoring.