Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction
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 a...
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| Published in | Expert systems with applications Vol. 36; no. 4; pp. 7390 - 7399 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.05.2009
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Poyraz, Mustafa Yildiz, Abdulnasir Kirbas, Gokhan Akin, Mehmet |
| Author_xml | – sequence: 1 givenname: Abdulnasir surname: Yildiz fullname: Yildiz, Abdulnasir email: abnayil@dicle.edu.tr organization: Department of Electrical and Electronics Engineering, University of Dicle, Diyarbakir, Turkey – sequence: 2 givenname: Mehmet surname: Akin fullname: Akin, Mehmet email: makin@dicle.edu.tr organization: Department of Electrical and Electronics Engineering, University of Dicle, Diyarbakir, Turkey – sequence: 3 givenname: Mustafa surname: Poyraz fullname: Poyraz, Mustafa email: mpoyraz@firat.edu.tr organization: Department of Electrical and Electronics Engineering University of Firat, Elazig, Turkey – sequence: 4 givenname: Gokhan surname: Kirbas fullname: Kirbas, Gokhan email: gkirbas@dicle.edu.tr organization: Faculty of Medicine, University of Dicle, Diyarbakir, Turkey |
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| Keywords | Discrete wavelet transform (DWT) Sleep Electroencephalogram (EEG) signals Adaptive neuro-fuzzy inference system (ANFIS) Drowsy Alert Entropy |
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| SubjectTerms | Adaptive neuro-fuzzy inference system (ANFIS) Alert Discrete wavelet transform (DWT) Drowsy Electroencephalogram (EEG) signals Entropy Sleep |
| Title | Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction |
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