Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients
This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradie...
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Published in | Journal of neuroscience methods Vol. 148; no. 2; pp. 113 - 121 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Netherlands
Elsevier B.V
30.10.2005
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Subjects | |
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Abstract | This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of the five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals. |
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AbstractList | This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of the five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals. |
Author | Übeyli, Elif Derya Güler, İnan |
Author_xml | – sequence: 1 givenname: Inan surname: Güler fullname: Güler, Inan email: iguler@gazi.edu.tr organization: Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, 06500 Teknikokullar, Ankara, Turkey. iguler@gazi.edu.tr – sequence: 2 givenname: Elif Derya surname: Ubeyli fullname: Ubeyli, Elif Derya |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16054702$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1016/S0165-1684(97)00038-8 10.1016/S0933-3657(01)00099-9 10.1016/S0933-3657(98)00068-2 10.1016/S0933-3657(98)00070-0 10.1109/10.24253 10.1109/18.57199 10.1109/IEMBS.2006.259789 10.1007/BF02350993 10.1016/S0925-2312(01)00648-8 10.1109/5.488704 10.1109/21.256541 10.1016/S0010-4825(03)00011-8 10.1103/PhysRevE.64.061907 10.1006/cbmr.1999.1517 10.1016/S0009-8981(97)00232-5 10.1016/S0957-4174(03)00002-2 10.1179/016164104773026534 10.1162/neco.1990.2.4.480 10.1109/72.159060 10.1111/j.1540-8159.1999.tb00329.x 10.1016/S0165-0270(02)00340-0 10.1016/0013-4694(93)90149-P 10.1007/BF02457822 10.1016/j.compbiomed.2004.03.003 10.1016/0013-4694(92)90086-W 10.1016/j.eswa.2004.05.001 |
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References | Daubechies (bib5) 1990; 36 Übeyli, Güler (bib20) 2003; 25 Baxt (bib3) 1990; 2 Soltani (bib19) 2002; 48 Übeyli ED, Güler İ. Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals. Comput Biol Med (2005b), in press. Andrzejak, Lehnertz, Mormann, Rieke, David, Elger (bib2) 2001; 64 Gabor, Seyal (bib7) 1992; 83 Belal, Taktak, Nevill, Spencer, Roden, Bevan (bib4) 2002; 24 Usher, Campbell, Vohra, Cameron (bib24) 1999; 22 Kuncheva, Steimann (bib14) 1999; 16 Nigam, Graupe (bib17) 2004; 26 Miller, Blott, Hames (bib15) 1992; 30 Dubois, Prade (bib6) 1998; 270 Unser, Aldroubi (bib23) 1996; 84 Virant-Klun, Virant (bib25) 1999; 32 Jang (bib12) 1992; 3 Übeyli, Güler (bib21) 2005; 35 Rosso, Figliola, Creso, Serrano (bib18) 2004; 42 Jang (bib13) 1993; 23 Nauck, Kruse (bib16) 1999; 16 Hazarika, Chen, Tsoi, Sergejew (bib11) 1997; 59 Glover, Raghaven, Ktonas, Frost (bib8) 1989; 36 Güler, Übeyli (bib10) 2004; 27 Adeli, Zhou, Dadmehr (bib1) 2003; 123 Güler, Übeyli (bib9) 2003; 33 Webber, Litt, Lesser, Fisher, Bankman (bib26) 1993; 87 Glover (10.1016/j.jneumeth.2005.04.013_bib8) 1989; 36 Jang (10.1016/j.jneumeth.2005.04.013_bib12) 1992; 3 Usher (10.1016/j.jneumeth.2005.04.013_bib24) 1999; 22 Hazarika (10.1016/j.jneumeth.2005.04.013_bib11) 1997; 59 Unser (10.1016/j.jneumeth.2005.04.013_bib23) 1996; 84 Daubechies (10.1016/j.jneumeth.2005.04.013_bib5) 1990; 36 Kuncheva (10.1016/j.jneumeth.2005.04.013_bib14) 1999; 16 10.1016/j.jneumeth.2005.04.013_bib22 Nigam (10.1016/j.jneumeth.2005.04.013_bib17) 2004; 26 Dubois (10.1016/j.jneumeth.2005.04.013_bib6) 1998; 270 Virant-Klun (10.1016/j.jneumeth.2005.04.013_bib25) 1999; 32 Übeyli (10.1016/j.jneumeth.2005.04.013_bib21) 2005; 35 Gabor (10.1016/j.jneumeth.2005.04.013_bib7) 1992; 83 Webber (10.1016/j.jneumeth.2005.04.013_bib26) 1993; 87 Nauck (10.1016/j.jneumeth.2005.04.013_bib16) 1999; 16 Miller (10.1016/j.jneumeth.2005.04.013_bib15) 1992; 30 Andrzejak (10.1016/j.jneumeth.2005.04.013_bib2) 2001; 64 Adeli (10.1016/j.jneumeth.2005.04.013_bib1) 2003; 123 Jang (10.1016/j.jneumeth.2005.04.013_bib13) 1993; 23 Güler (10.1016/j.jneumeth.2005.04.013_bib9) 2003; 33 Belal (10.1016/j.jneumeth.2005.04.013_bib4) 2002; 24 Rosso (10.1016/j.jneumeth.2005.04.013_bib18) 2004; 42 Soltani (10.1016/j.jneumeth.2005.04.013_bib19) 2002; 48 Baxt (10.1016/j.jneumeth.2005.04.013_bib3) 1990; 2 Güler (10.1016/j.jneumeth.2005.04.013_bib10) 2004; 27 Übeyli (10.1016/j.jneumeth.2005.04.013_bib20) 2003; 25 |
References_xml | – volume: 2 start-page: 480 year: 1990 end-page: 489 ident: bib3 article-title: Use of an artificial neural network for data analysis in clinical decision making: the diagnosis of acute coronary occlusion publication-title: Neural Comput contributor: fullname: Baxt – volume: 42 start-page: 516 year: 2004 end-page: 523 ident: bib18 article-title: Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings publication-title: Med Biol Eng Comput contributor: fullname: Serrano – volume: 22 start-page: 183 year: 1999 end-page: 186 ident: bib24 article-title: A fuzzy logic-controlled classifier for use in implantable cardioverter defibrillators publication-title: Pace-Pacing Clin Electrophysiol contributor: fullname: Cameron – volume: 84 start-page: 626 year: 1996 end-page: 638 ident: bib23 article-title: A review of wavelets in biomedical applications publication-title: Proc IEEE contributor: fullname: Aldroubi – volume: 27 start-page: 323 year: 2004 end-page: 330 ident: bib10 article-title: Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction publication-title: Expert Syst Appl contributor: fullname: Übeyli – volume: 87 start-page: 364 year: 1993 end-page: 373 ident: bib26 article-title: Automatic EEG spike detection: what should the computer imitate? publication-title: Electroencephalogr Clin Neurophysiol contributor: fullname: Bankman – volume: 3 start-page: 714 year: 1992 end-page: 723 ident: bib12 article-title: Self-learning fuzzy controllers based on temporal backpropagation publication-title: IEEE Trans Neural Netw contributor: fullname: Jang – volume: 26 start-page: 55 year: 2004 end-page: 60 ident: bib17 article-title: A neural-network-based detection of epilepsy publication-title: Neurol Res contributor: fullname: Graupe – volume: 16 start-page: 149 year: 1999 end-page: 169 ident: bib16 article-title: Obtaining interpretable fuzzy classification rules from medical data publication-title: Artif Intell Med contributor: fullname: Kruse – volume: 24 start-page: 149 year: 2002 end-page: 165 ident: bib4 article-title: Automatic detection of distorted plethysmogram pulses in neonates and paediatric patients using an adaptive-network-based fuzzy inference system publication-title: Artif Intell Med contributor: fullname: Bevan – volume: 59 start-page: 61 year: 1997 end-page: 72 ident: bib11 article-title: Classification of EEG signals using the wavelet transform publication-title: Signal Process contributor: fullname: Sergejew – volume: 123 start-page: 69 year: 2003 end-page: 87 ident: bib1 article-title: Analysis of EEG records in an epileptic patient using wavelet transform publication-title: J Neurosci Methods contributor: fullname: Dadmehr – volume: 36 start-page: 519 year: 1989 end-page: 527 ident: bib8 article-title: Context-based automated detection of epileptogenic sharp transients in the EEG: elimination of false positives publication-title: IEEE Trans Biomed Eng contributor: fullname: Frost – volume: 23 start-page: 665 year: 1993 end-page: 685 ident: bib13 article-title: ANFIS: Adaptive-network-based fuzzy inference system publication-title: IEEE Trans Syst Man Cybern contributor: fullname: Jang – volume: 83 start-page: 271 year: 1992 end-page: 280 ident: bib7 article-title: Automated interictal EEG spike detection using artificial neural networks publication-title: Electroencephalogr Clin Neurophysiol contributor: fullname: Seyal – volume: 16 start-page: 121 year: 1999 end-page: 128 ident: bib14 article-title: Fuzzy diagnosis publication-title: Artif Intell Med contributor: fullname: Steimann – volume: 35 start-page: 421 year: 2005 end-page: 433 ident: bib21 article-title: Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems publication-title: Comput Biol Med contributor: fullname: Güler – volume: 36 start-page: 961 year: 1990 end-page: 1005 ident: bib5 article-title: The wavelet transform, time-frequency localization and signal analysis publication-title: IEEE T Inform Theory contributor: fullname: Daubechies – volume: 25 start-page: 1 year: 2003 end-page: 13 ident: bib20 article-title: Neural network analysis of internal carotid arterial Doppler signals: Predictions of stenosis and occlusion publication-title: Expert Syst Appl contributor: fullname: Güler – volume: 64 start-page: 061907 year: 2001 ident: bib2 article-title: Indications of nonlinear deterministic and finite-dimensional structures in time seires of brain electrical activity: dependence on recording region and brain state publication-title: Phys Rev E contributor: fullname: Elger – volume: 30 start-page: 449 year: 1992 end-page: 464 ident: bib15 article-title: Review of neural network applications in medical imaging and signal processing publication-title: Med Biol Eng Comput contributor: fullname: Hames – volume: 32 start-page: 305 year: 1999 end-page: 321 ident: bib25 article-title: Fuzzy logic alternative for analysis in the biomedical sciences publication-title: Comput Biomed Res contributor: fullname: Virant – volume: 33 start-page: 333 year: 2003 end-page: 343 ident: bib9 article-title: Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network publication-title: Comput Biol Med contributor: fullname: Übeyli – volume: 48 start-page: 267 year: 2002 end-page: 277 ident: bib19 article-title: On the use of the wavelet decomposition for time series prediction publication-title: Neurocomputing contributor: fullname: Soltani – volume: 270 start-page: 3 year: 1998 end-page: 29 ident: bib6 article-title: An introduction to fuzzy systems publication-title: Clin Chim Acta contributor: fullname: Prade – volume: 59 start-page: 61 issue: 1 year: 1997 ident: 10.1016/j.jneumeth.2005.04.013_bib11 article-title: Classification of EEG signals using the wavelet transform publication-title: Signal Process doi: 10.1016/S0165-1684(97)00038-8 contributor: fullname: Hazarika – volume: 24 start-page: 149 year: 2002 ident: 10.1016/j.jneumeth.2005.04.013_bib4 article-title: Automatic detection of distorted plethysmogram pulses in neonates and paediatric patients using an adaptive-network-based fuzzy inference system publication-title: Artif Intell Med doi: 10.1016/S0933-3657(01)00099-9 contributor: fullname: Belal – volume: 16 start-page: 121 year: 1999 ident: 10.1016/j.jneumeth.2005.04.013_bib14 article-title: Fuzzy diagnosis publication-title: Artif Intell Med doi: 10.1016/S0933-3657(98)00068-2 contributor: fullname: Kuncheva – volume: 16 start-page: 149 year: 1999 ident: 10.1016/j.jneumeth.2005.04.013_bib16 article-title: Obtaining interpretable fuzzy classification rules from medical data publication-title: Artif Intell Med doi: 10.1016/S0933-3657(98)00070-0 contributor: fullname: Nauck – volume: 36 start-page: 519 issue: 5 year: 1989 ident: 10.1016/j.jneumeth.2005.04.013_bib8 article-title: Context-based automated detection of epileptogenic sharp transients in the EEG: elimination of false positives publication-title: IEEE Trans Biomed Eng doi: 10.1109/10.24253 contributor: fullname: Glover – volume: 36 start-page: 961 issue: 5 year: 1990 ident: 10.1016/j.jneumeth.2005.04.013_bib5 article-title: The wavelet transform, time-frequency localization and signal analysis publication-title: IEEE T Inform Theory doi: 10.1109/18.57199 contributor: fullname: Daubechies – ident: 10.1016/j.jneumeth.2005.04.013_bib22 doi: 10.1109/IEMBS.2006.259789 – volume: 42 start-page: 516 issue: 4 year: 2004 ident: 10.1016/j.jneumeth.2005.04.013_bib18 article-title: Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings publication-title: Med Biol Eng Comput doi: 10.1007/BF02350993 contributor: fullname: Rosso – volume: 48 start-page: 267 year: 2002 ident: 10.1016/j.jneumeth.2005.04.013_bib19 article-title: On the use of the wavelet decomposition for time series prediction publication-title: Neurocomputing doi: 10.1016/S0925-2312(01)00648-8 contributor: fullname: Soltani – volume: 84 start-page: 626 issue: 4 year: 1996 ident: 10.1016/j.jneumeth.2005.04.013_bib23 article-title: A review of wavelets in biomedical applications publication-title: Proc IEEE doi: 10.1109/5.488704 contributor: fullname: Unser – volume: 23 start-page: 665 issue: 3 year: 1993 ident: 10.1016/j.jneumeth.2005.04.013_bib13 article-title: ANFIS: Adaptive-network-based fuzzy inference system publication-title: IEEE Trans Syst Man Cybern doi: 10.1109/21.256541 contributor: fullname: Jang – volume: 33 start-page: 333 issue: 4 year: 2003 ident: 10.1016/j.jneumeth.2005.04.013_bib9 article-title: Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network publication-title: Comput Biol Med doi: 10.1016/S0010-4825(03)00011-8 contributor: fullname: Güler – volume: 64 start-page: 061907 year: 2001 ident: 10.1016/j.jneumeth.2005.04.013_bib2 article-title: Indications of nonlinear deterministic and finite-dimensional structures in time seires of brain electrical activity: dependence on recording region and brain state publication-title: Phys Rev E doi: 10.1103/PhysRevE.64.061907 contributor: fullname: Andrzejak – volume: 32 start-page: 305 year: 1999 ident: 10.1016/j.jneumeth.2005.04.013_bib25 article-title: Fuzzy logic alternative for analysis in the biomedical sciences publication-title: Comput Biomed Res doi: 10.1006/cbmr.1999.1517 contributor: fullname: Virant-Klun – volume: 270 start-page: 3 year: 1998 ident: 10.1016/j.jneumeth.2005.04.013_bib6 article-title: An introduction to fuzzy systems publication-title: Clin Chim Acta doi: 10.1016/S0009-8981(97)00232-5 contributor: fullname: Dubois – volume: 25 start-page: 1 issue: 1 year: 2003 ident: 10.1016/j.jneumeth.2005.04.013_bib20 article-title: Neural network analysis of internal carotid arterial Doppler signals: Predictions of stenosis and occlusion publication-title: Expert Syst Appl doi: 10.1016/S0957-4174(03)00002-2 contributor: fullname: Übeyli – volume: 26 start-page: 55 issue: 1 year: 2004 ident: 10.1016/j.jneumeth.2005.04.013_bib17 article-title: A neural-network-based detection of epilepsy publication-title: Neurol Res doi: 10.1179/016164104773026534 contributor: fullname: Nigam – volume: 2 start-page: 480 year: 1990 ident: 10.1016/j.jneumeth.2005.04.013_bib3 article-title: Use of an artificial neural network for data analysis in clinical decision making: the diagnosis of acute coronary occlusion publication-title: Neural Comput doi: 10.1162/neco.1990.2.4.480 contributor: fullname: Baxt – volume: 3 start-page: 714 issue: 5 year: 1992 ident: 10.1016/j.jneumeth.2005.04.013_bib12 article-title: Self-learning fuzzy controllers based on temporal backpropagation publication-title: IEEE Trans Neural Netw doi: 10.1109/72.159060 contributor: fullname: Jang – volume: 22 start-page: 183 year: 1999 ident: 10.1016/j.jneumeth.2005.04.013_bib24 article-title: A fuzzy logic-controlled classifier for use in implantable cardioverter defibrillators publication-title: Pace-Pacing Clin Electrophysiol doi: 10.1111/j.1540-8159.1999.tb00329.x contributor: fullname: Usher – volume: 123 start-page: 69 issue: 1 year: 2003 ident: 10.1016/j.jneumeth.2005.04.013_bib1 article-title: Analysis of EEG records in an epileptic patient using wavelet transform publication-title: J Neurosci Methods doi: 10.1016/S0165-0270(02)00340-0 contributor: fullname: Adeli – volume: 87 start-page: 364 issue: 6 year: 1993 ident: 10.1016/j.jneumeth.2005.04.013_bib26 article-title: Automatic EEG spike detection: what should the computer imitate? publication-title: Electroencephalogr Clin Neurophysiol doi: 10.1016/0013-4694(93)90149-P contributor: fullname: Webber – volume: 30 start-page: 449 year: 1992 ident: 10.1016/j.jneumeth.2005.04.013_bib15 article-title: Review of neural network applications in medical imaging and signal processing publication-title: Med Biol Eng Comput doi: 10.1007/BF02457822 contributor: fullname: Miller – volume: 35 start-page: 421 issue: 5 year: 2005 ident: 10.1016/j.jneumeth.2005.04.013_bib21 article-title: Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2004.03.003 contributor: fullname: Übeyli – volume: 83 start-page: 271 issue: 5 year: 1992 ident: 10.1016/j.jneumeth.2005.04.013_bib7 article-title: Automated interictal EEG spike detection using artificial neural networks publication-title: Electroencephalogr Clin Neurophysiol doi: 10.1016/0013-4694(92)90086-W contributor: fullname: Gabor – volume: 27 start-page: 323 issue: 3 year: 2004 ident: 10.1016/j.jneumeth.2005.04.013_bib10 article-title: Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction publication-title: Expert Syst Appl doi: 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SubjectTerms | Adaptive neuro-fuzzy inference system (ANFIS) Artificial Intelligence Brain - physiology Electroencephalogram (EEG) signals Electroencephalography - methods Evoked Potentials - physiology Fuzzy Logic Humans Neural Networks (Computer) Signal Processing, Computer-Assisted Software Wavelet transform |
Title | Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients |
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