A novel data reduction method: Distance based data reduction and its application to classification of epileptiform EEG signals

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dc.contributor.authorPolat, Kemal
dc.contributor.authorGuenes, Salih
dc.date.accessioned2020-03-26T17:26:20Z
dc.date.available2020-03-26T17:26:20Z
dc.date.issued2008
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractObjective: Data reduction methods are a crucial step affecting both performance and computation time of classification systems in pattern recognition applications such as medical decision making systems, intelligent control, and data clustering. The aim of this study is both to increase the classification accuracy and decrease the computation time of classifier system on the classification of epileptiform EEG signals. Methods: In this study, we have proposed a novel data reduction method based on distances between groups data double in all dataset and applied this method to the classification of epileptiform EEG signals. The feature extraction methods including autoregressive (AR), discrete Fourier transform (DFT), and discrete wavelet transform (DWT), distance based data reduction, and C4.5 decision tree classifier have been combined to classify the epileptiform EEG signals. As feature extraction part AR, DFT, and DWT methods have been used to determine the features about EEG signals including epileptic seizure patients and eyes open volunteers. As data pre-processing part, distance based data reduction that is proposed firstly by us has been used to reduce data determined by spectral analysis methods (AR, DFT, and DWT). As final part called classification, C4.5 decision tree classifier has been used to classify reduced epileptiform EEG signals. Results: To validate and test the proposed data reduction, the classification accuracy, sensitivity, and specifity analysis, computation time, 10-fold cross-validation, and 95% confidence intervals have been used in this study. Six different combined methods have been used to classify the epileptiform EEG signal. These methods are (i) combining DFT and C4.5 decision tree classifier (DCT), (ii) combining DFT, distance based data reduction, and C4.5 DCT, (iii) combining AR and C4.5 DCT, (iv) combining AR, distance based data reduction, and C4.5 DCT, ( v) combining DWT and C4.5 DCT, and ( vi) combining DWT, distance based data reduction, and C4.5 DCT. The classification accuracies and computation times obtained by these methods are 99.02%-79 s, 99.12%-47 s, 99.32%-65 s, 98.94%-45 s, 92.00%-52.06 s, and 89.50%-29.9 s. Conclusions: These results have shown that the proposed distance based data reduction method has produced very promising results with respect to both classification accuracy and computation time for classifying the epileptiform EEG signals. Also, proposed hybrid systems can be used to detect the epileptic seizure. (C) 2007 Elsevier Inc. All rights reserved.en_US
dc.identifier.doi10.1016/j.amc.2007.12.028en_US
dc.identifier.endpage27en_US
dc.identifier.issn0096-3003en_US
dc.identifier.issn1873-5649en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage10en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.amc.2007.12.028
dc.identifier.urihttps://hdl.handle.net/20.500.12395/22171
dc.identifier.volume200en_US
dc.identifier.wosWOS:000255728600002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE INCen_US
dc.relation.ispartofAPPLIED MATHEMATICS AND COMPUTATIONen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectEEG signalsen_US
dc.subjectdistance based data reductionen_US
dc.subjectAR spectral analysisen_US
dc.subjectdiscrete Fourier transformen_US
dc.subjectdiscrete wavelet transform (DWT)en_US
dc.subjectC4.5 decision tree classifieren_US
dc.subjectepileptic seizureen_US
dc.titleA novel data reduction method: Distance based data reduction and its application to classification of epileptiform EEG signalsen_US
dc.typeArticleen_US

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