Classification of EEG signals using the wavelet transform
Signal Processing
Fuzzy logic alternative for analysis in the biomedical sciences
Computers and Biomedical Research
Computers in Biology and Medicine
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
Expert Systems with Applications: An International Journal
A machine learning approach to classify vigilance states in rats
Expert Systems with Applications: An International Journal
Review article: Human scalp EEG processing: Various soft computing approaches
Applied Soft Computing
Computer Methods and Programs in Biomedicine
International Journal of Bioinformatics Research and Applications
Integrated Computer-Aided Engineering
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This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) model for estimation of vigilance level by using electroencephalogram (EEG) signals recorded during transition from wakefulness to sleep. The developed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. This study comprises of three stages. In the first stage, three types of EEG signals (alert signal, drowsy signal and sleep signal) were obtained from 30 healthy subjects. In the second stage, for feature extraction, obtained EEG signals were separated to its sub-bands using discrete wavelet transform (DWT). Then, entropy of each sub-band was calculated using Shannon entropy algorithm. In the third stage, the ANFIS was trained with the back-propagation gradient descent method in combination with least squares method. The extracted features of three types of EEG signals were used as input patterns of the three ANFIS classifiers. In order to improve estimation accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The performance of the ANFIS model was tested using the EEG data obtained from 12 healthy subjects that have not been used for the training. The results confirmed that the developed ANFIS classifier has potential for estimation of vigilance level by using EEG signals.