Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform
The aim of this study is to detect epileptic seizure in EEG signals using a hybrid system based on decision tree classifier and fast Fourier transform (FFT). The present study proposes a hybrid system with two stages: feature extraction using FFT and decision making using decision tree classifier. T...
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| Vydané v: | Applied mathematics and computation Ročník 187; číslo 2; s. 1017 - 1026 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York, NY
Elsevier Inc
15.04.2007
Elsevier |
| Predmet: | |
| ISSN: | 0096-3003, 1873-5649 |
| On-line prístup: | Získať plný text |
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| Abstract | The aim of this study is to detect epileptic seizure in EEG signals using a hybrid system based on decision tree classifier and fast Fourier transform (FFT). The present study proposes a hybrid system with two stages: feature extraction using FFT and decision making using decision tree classifier. The detection of epileptiform discharges in the electroencephalogram (EEG) is an important part in the diagnosis of epilepsy. All data set were obtained from EEG signals of healthy subjects and subjects suffering from epilepsy diseases. For healthy subjects is background EEG (scalp) with open eyes and for epileptic patients correspond to a seizure recorded in hippocampus (epileptic focus) with depth electrodes. The evolution of proposed system was conducted using
k-fold cross-validation, classification accuracy, and sensitivity and specificity values. We have obtained 98.68% and 98.72% classification accuracies using 5- and 10-fold cross-validation. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system. |
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| AbstractList | The aim of this study is to detect epileptic seizure in EEG signals using a hybrid system based on decision tree classifier and fast Fourier transform (FFT). The present study proposes a hybrid system with two stages: feature extraction using FFT and decision making using decision tree classifier. The detection of epileptiform discharges in the electroencephalogram (EEG) is an important part in the diagnosis of epilepsy. All data set were obtained from EEG signals of healthy subjects and subjects suffering from epilepsy diseases. For healthy subjects is background EEG (scalp) with open eyes and for epileptic patients correspond to a seizure recorded in hippocampus (epileptic focus) with depth electrodes. The evolution of proposed system was conducted using
k-fold cross-validation, classification accuracy, and sensitivity and specificity values. We have obtained 98.68% and 98.72% classification accuracies using 5- and 10-fold cross-validation. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system. |
| Author | Güneş, Salih Polat, Kemal |
| Author_xml | – sequence: 1 givenname: Kemal surname: Polat fullname: Polat, Kemal email: kpolat@selcuk.edu.tr – sequence: 2 givenname: Salih surname: Güneş fullname: Güneş, Salih email: sgunes@selcuk.edu.tr |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18739071$$DView record in Pascal Francis |
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| CODEN | AMHCBQ |
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| Cites_doi | 10.1016/0013-4694(92)90086-W 10.1016/S0301-5629(00)00153-8 10.1016/S0165-0270(02)00340-0 10.1016/S0165-1684(97)00038-8 10.1016/0301-5629(88)90024-5 10.1016/S0370-1573(99)00096-4 10.1097/00004691-200101000-00010 10.1016/0013-4694(93)90149-P 10.1007/BF00116251 10.1016/S0010-4825(02)00036-7 10.1103/PhysRevE.64.061907 10.1016/j.cmpb.2005.06.012 10.1179/016164104773026534 10.1016/j.eswa.2005.04.011 10.1007/BF02350993 10.1016/j.jneumeth.2005.04.013 10.1023/A:1007442505281 10.1109/10.24253 |
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| Keywords | Decision tree classifier k-Fold cross-validation Electroencephalogram (EEG) FFT Epileptic seizure Disease Fast Fourier transformation Cross validation Classifier Decision tree Depth Hybrid system Design Electrodes Accuracy Classification Evolution Diagnosis Intelligent system Decision making Cross classification Sensitivity Numerical analysis Applied mathematics Experimental design Feature extraction Detection Hippocampus |
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| References | Glass, Michel, Mackey, Shrier (bib3) 1983; 13 A. Subasi, EEG signal classification using wavelet feature extraction and a mixture of expert model, Expert Systems with Applications, in press. Guler, Ubeyli (bib14) 2005; 148 Adeli, Zhou, Dadmehr (bib7) 2003; 123 Evans (bib17) 2000; 26 Duke, Pritchard (bib1) 1991 Andrzejak, Lehnertz, Mormann, Rieke, David, Elger (bib15) 2001; 64 Guler, Hardalac, Kaymaz (bib19) 2002; 32 Boccaletti, Grebogi, Lai, Mancini, Mazaet (bib2) 2000; 329 Guler, Ubeyli, Guler (bib23) 2005; 29 Jaeseung, Jeong-Ho, Kim, Seol-Heui (bib4) 2001; 18 Philippe, Henri (bib5) 2001; 324 Nigam, Graupe (bib13) 2004; 26 Hazarika, Chen, Tsoi, Sergejew (bib8) 1997; 59 Mitchell (bib20) 1997 Quinlan (bib21) 1986; 1 Glover, Raghaven, Ktonas, Frost (bib10) 1989; 36 Webber, Litt, Lesser, Fisher, Bankman (bib12) 1993; 87 Kannathal, Choo, Rajendra Acharya, Sadasivana (bib6) 2005; 80 R. Kohavi, F. Provost, Glossary of terms. Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process, 30(2/3) (1998). Gabor, Seyal (bib11) 1992; 83 Rosso, Figliola, Creso, Serrano (bib9) 2004; 42 Vaitkus, Cobbold, Johnston (bib18) 1988; 14 10.1016/j.amc.2006.09.022_bib16 Rosso (10.1016/j.amc.2006.09.022_bib9) 2004; 42 Evans (10.1016/j.amc.2006.09.022_bib17) 2000; 26 Kannathal (10.1016/j.amc.2006.09.022_bib6) 2005; 80 Quinlan (10.1016/j.amc.2006.09.022_bib21) 1986; 1 10.1016/j.amc.2006.09.022_bib22 Mitchell (10.1016/j.amc.2006.09.022_bib20) 1997 Glass (10.1016/j.amc.2006.09.022_bib3) 1983; 13 Glover (10.1016/j.amc.2006.09.022_bib10) 1989; 36 Guler (10.1016/j.amc.2006.09.022_bib14) 2005; 148 Webber (10.1016/j.amc.2006.09.022_bib12) 1993; 87 Jaeseung (10.1016/j.amc.2006.09.022_bib4) 2001; 18 Gabor (10.1016/j.amc.2006.09.022_bib11) 1992; 83 Duke (10.1016/j.amc.2006.09.022_bib1) 1991 Nigam (10.1016/j.amc.2006.09.022_bib13) 2004; 26 Philippe (10.1016/j.amc.2006.09.022_bib5) 2001; 324 Andrzejak (10.1016/j.amc.2006.09.022_bib15) 2001; 64 Guler (10.1016/j.amc.2006.09.022_bib19) 2002; 32 Vaitkus (10.1016/j.amc.2006.09.022_bib18) 1988; 14 Guler (10.1016/j.amc.2006.09.022_bib23) 2005; 29 Adeli (10.1016/j.amc.2006.09.022_bib7) 2003; 123 Hazarika (10.1016/j.amc.2006.09.022_bib8) 1997; 59 Boccaletti (10.1016/j.amc.2006.09.022_bib2) 2000; 329 |
| References_xml | – year: 1991 ident: bib1 article-title: Measuring Chaos in the Human Brain – volume: 14 start-page: 673 year: 1988 end-page: 688 ident: bib18 article-title: A comparative study and assessment of Doppler ultrasound spectral estimation techniques part II: methods and results publication-title: Ultrasound Med. Biol. – volume: 80 start-page: 187 year: 2005 end-page: 194 ident: bib6 article-title: Entropies for detection of epilepsy in EEG publication-title: Comput. Methods Programs Biomed. – volume: 1 start-page: 81 year: 1986 end-page: 106 ident: bib21 article-title: Induction of decision trees publication-title: Mach. Learn. – volume: 324 start-page: 773 year: 2001 end-page: 793 ident: bib5 article-title: Is there chaos in the brain? Concepts of nonlinear dynamics and methods of investigation publication-title: Life Sci. – volume: 26 start-page: 55 year: 2004 end-page: 60 ident: bib13 article-title: A neural-network-based detection of epilepsy publication-title: Neurol. Res. – reference: R. Kohavi, F. Provost, Glossary of terms. Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process, 30(2/3) (1998). – volume: 329 start-page: 108 year: 2000 end-page: 109 ident: bib2 article-title: The control of chaos: theory and applications publication-title: Phys. Rep. – volume: 36 start-page: 519 year: 1989 end-page: 527 ident: bib10 article-title: Context-based automated detection of epileptogenic sharp transients in the EEG: elimination of false positives publication-title: IEEE Trans. Biomed. Eng. – volume: 148 start-page: 113 year: 2005 end-page: 121 ident: bib14 article-title: Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients publication-title: J. Neurosci. Methods – year: 1997 ident: bib20 article-title: Machine Learning – volume: 64 start-page: 061907 year: 2001 ident: bib15 article-title: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state publication-title: Phys. Rev. E – reference: A. Subasi, EEG signal classification using wavelet feature extraction and a mixture of expert model, Expert Systems with Applications, in press. – volume: 13 start-page: 790 year: 1983 end-page: 798 ident: bib3 article-title: Chaos in neurobiology publication-title: IEEE Trans. Syst. Man Cybern. SMC – volume: 42 start-page: 516 year: 2004 end-page: 523 ident: bib9 article-title: Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings publication-title: Med. Biol. Eng. Comput. – volume: 123 start-page: 69 year: 2003 end-page: 87 ident: bib7 article-title: Analysis of EEG records in an epileptic patient using wavelet transform publication-title: J. Neurosci. Methods – volume: 59 start-page: 61 year: 1997 end-page: 72 ident: bib8 article-title: Classification of EEG signals using the wavelet transform publication-title: Signal Process. – volume: 18 start-page: 58 year: 2001 end-page: 67 ident: bib4 article-title: Nonlinear dynamical analysis of the EEG in patients with Alzheimer’s disease and vacular dementia publication-title: Clin. Neurophysiol. – volume: 32 start-page: 445 year: 2002 end-page: 453 ident: bib19 article-title: Comparison of FFT and adaptive ARMA methods in transcranial Doppler signals recorded from the cerebral vessels publication-title: Comput. Biol. Med. – volume: 26 start-page: S13 year: 2000 end-page: S15 ident: bib17 article-title: Doppler signal analysis publication-title: Ultrasound Med. Biol. – volume: 29 start-page: 506 year: 2005 end-page: 514 ident: bib23 article-title: Recurrent neural networks employing Lyapunov exponents for EEG signals classification publication-title: Expert Syst. Appl. – volume: 83 start-page: 271 year: 1992 end-page: 280 ident: bib11 article-title: Automated interictal EEG spike detection using artificial neural networks publication-title: Electroencephalogr. Clin. Neurophysiol. – volume: 87 start-page: 364 year: 1993 end-page: 373 ident: bib12 article-title: Automatic EEG spike detection: what should the computer imitate? publication-title: Electroencephalogr. Clin. Neurophysiol. – volume: 83 start-page: 271 issue: 5 year: 1992 ident: 10.1016/j.amc.2006.09.022_bib11 article-title: Automated interictal EEG spike detection using artificial neural networks publication-title: Electroencephalogr. Clin. Neurophysiol. doi: 10.1016/0013-4694(92)90086-W – volume: 26 start-page: S13 issue: Supplement 1 year: 2000 ident: 10.1016/j.amc.2006.09.022_bib17 article-title: Doppler signal analysis publication-title: Ultrasound Med. Biol. doi: 10.1016/S0301-5629(00)00153-8 – volume: 123 start-page: 69 issue: 1 year: 2003 ident: 10.1016/j.amc.2006.09.022_bib7 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 – volume: 59 start-page: 61 issue: 1 year: 1997 ident: 10.1016/j.amc.2006.09.022_bib8 article-title: Classification of EEG signals using the wavelet transform publication-title: Signal Process. doi: 10.1016/S0165-1684(97)00038-8 – year: 1991 ident: 10.1016/j.amc.2006.09.022_bib1 – volume: 14 start-page: 673 year: 1988 ident: 10.1016/j.amc.2006.09.022_bib18 article-title: A comparative study and assessment of Doppler ultrasound spectral estimation techniques part II: methods and results publication-title: Ultrasound Med. Biol. doi: 10.1016/0301-5629(88)90024-5 – volume: 329 start-page: 108 year: 2000 ident: 10.1016/j.amc.2006.09.022_bib2 article-title: The control of chaos: theory and applications publication-title: Phys. Rep. doi: 10.1016/S0370-1573(99)00096-4 – volume: 18 start-page: 58 issue: 1 year: 2001 ident: 10.1016/j.amc.2006.09.022_bib4 article-title: Nonlinear dynamical analysis of the EEG in patients with Alzheimer’s disease and vacular dementia publication-title: Clin. Neurophysiol. doi: 10.1097/00004691-200101000-00010 – volume: 87 start-page: 364 issue: 6 year: 1993 ident: 10.1016/j.amc.2006.09.022_bib12 article-title: Automatic EEG spike detection: what should the computer imitate? publication-title: Electroencephalogr. Clin. Neurophysiol. doi: 10.1016/0013-4694(93)90149-P – volume: 1 start-page: 81 year: 1986 ident: 10.1016/j.amc.2006.09.022_bib21 article-title: Induction of decision trees publication-title: Mach. Learn. doi: 10.1007/BF00116251 – volume: 13 start-page: 790 issue: 5 year: 1983 ident: 10.1016/j.amc.2006.09.022_bib3 article-title: Chaos in neurobiology publication-title: IEEE Trans. Syst. Man Cybern. SMC – volume: 32 start-page: 445 year: 2002 ident: 10.1016/j.amc.2006.09.022_bib19 article-title: Comparison of FFT and adaptive ARMA methods in transcranial Doppler signals recorded from the cerebral vessels publication-title: Comput. Biol. Med. doi: 10.1016/S0010-4825(02)00036-7 – volume: 64 start-page: 061907 year: 2001 ident: 10.1016/j.amc.2006.09.022_bib15 article-title: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.64.061907 – volume: 324 start-page: 773 year: 2001 ident: 10.1016/j.amc.2006.09.022_bib5 article-title: Is there chaos in the brain? Concepts of nonlinear dynamics and methods of investigation publication-title: Life Sci. – volume: 80 start-page: 187 year: 2005 ident: 10.1016/j.amc.2006.09.022_bib6 article-title: Entropies for detection of epilepsy in EEG publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2005.06.012 – ident: 10.1016/j.amc.2006.09.022_bib16 – volume: 26 start-page: 55 issue: 1 year: 2004 ident: 10.1016/j.amc.2006.09.022_bib13 article-title: A neural-network-based detection of epilepsy publication-title: Neurol. Res. doi: 10.1179/016164104773026534 – year: 1997 ident: 10.1016/j.amc.2006.09.022_bib20 – volume: 29 start-page: 506 year: 2005 ident: 10.1016/j.amc.2006.09.022_bib23 article-title: Recurrent neural networks employing Lyapunov exponents for EEG signals classification publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2005.04.011 – volume: 42 start-page: 516 issue: 4 year: 2004 ident: 10.1016/j.amc.2006.09.022_bib9 article-title: Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings publication-title: Med. Biol. Eng. Comput. doi: 10.1007/BF02350993 – volume: 148 start-page: 113 year: 2005 ident: 10.1016/j.amc.2006.09.022_bib14 article-title: Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2005.04.013 – ident: 10.1016/j.amc.2006.09.022_bib22 doi: 10.1023/A:1007442505281 – volume: 36 start-page: 519 issue: 5 year: 1989 ident: 10.1016/j.amc.2006.09.022_bib10 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 |
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| SubjectTerms | Calculus of variations and optimal control Combinatorics Combinatorics. Ordered structures Decision tree classifier Designs and configurations Electroencephalogram (EEG) Epileptic seizure Exact sciences and technology FFT k-Fold cross-validation Mathematical analysis Mathematics Numerical analysis Numerical analysis. Scientific computation Sciences and techniques of general use |
| Title | Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform |
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