Journal of Medical Systems
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Genetic fuzzy classifier for sleep stage identification
Computers in Biology and Medicine
Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging
Computers in Biology and Medicine
Singular Spectrum Analysis of Sleep EEG in Insomnia
Journal of Medical Systems
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
Automatic classification of sleep stages based on the time-frequency image of EEG signals
Computer Methods and Programs in Biomedicine
Hi-index | 0.01 |
This paper presents a recurrent neural classifier for automatically classifying sleep stages based on energy features from the EEG signal of the F"p"z-C"z channel. The energy features were extracted from characteristic waves of EEG signals which were then used to classify different sleep stages. The recurrent neural classifier, utilizing energy features extracted from EEG signals, assigned each 30-s epoch to one of five possible sleep stages: wakefulness, NREM 1, NREM 2, SWS, and REM. Eight sleep recordings obtained from the Sleep-EDF database, which is available from the PhysioBank, were utilized to validate the proposed method. Using the features extracted by our research, classification performance of a feedforward neural network (FNN) and a probabilistic neural network (PNN) were compared to that of the proposed recurrent neural classifier. The classification rate of the recurrent neural classifier was found to be better (87.2%) than those of the two neural classifiers (81.1% for FNN and 81.8% for PNN). The result demonstrates that the proposed recurrent neural classifier using the energy features extracted from characteristic waves of EEG signals can classify sleep stages more efficiently and accurately using only a single EEG channel.