A classification approach for focal/non-focal EEG detection using cepstral analysis

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dc.authorid0000-0002-9368-8902en_US
dc.authorid0000-0002-6863-7150en_US
dc.contributor.authorŞeker, Delal
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.date.accessioned2022-01-28T12:55:03Z
dc.date.available2022-01-28T12:55:03Z
dc.date.issued2021en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractElectroencephalogram (EEG) is a convenient neuroimaging technique due to its non-invasive setup, practical usage, and high temporal resolution. EEG allows to detect brain electrical activity to diagnose neurological disorders. Epilepsy is a crucial neurologic disorder that is reasoned from occurrence of sudden and repeated seizures. The goal of this paper is to classify the focal (epileptogenic area) and non-focal (non-epileptogenic area) EEG records with cepstral coefficients and machine learning algorithms. Analysis is carried out using publicly available Bern-Barcelona EEG dataset. Mel Frequency Cepstral Coefficients (MFCC) are calculated from EEG epochs. Feature sets are normalized with z-score and dimension reduction is realized using Principal Component Analysis. Fine Tree, Quadratic Discriminant Analysis, Logistic Regression, Gaussian Naïve Bayes, Cubic Support Vector Machine, weighted k-nearest neighbors, and Bagged Trees are applied for classification stage. A value of k=10 is used for cross validation. All focal and non-focal EEG pairs are perfectly classified with acc., sen., spe., and F1-score of 100% and AUC with 1 via. Quadratic Discriminant Analysis, Logistic Regression, Cubic SVM and Weighted k-NN. Proposed work recommends MFCCs as a single marker and this provides less computation workload, practicality, and direct processing of focal / non-focal EEG time series. Proposed methodology in this paper serves one of the highest achievements to literature and can assist neurologist and physicians to validate their diagnosis.en_US
dc.identifier.citationŞeker, D. ve Özerdem, M. S. (2021). A classification approach for focal/non-focal EEG detection using cepstral analysis. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 12(4), 603-613.en_US
dc.identifier.doi10.24012/dumf.1002081
dc.identifier.endpage613en_US
dc.identifier.issn1309-8640
dc.identifier.issn2146-4391
dc.identifier.issue4en_US
dc.identifier.startpage603en_US
dc.identifier.trdizinid482600
dc.identifier.urihttps://dergipark.org.tr/tr/download/article-file/1999734
dc.identifier.urihttps://hdl.handle.net/11468/9115
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/482600
dc.identifier.volume12en_US
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorŞeker, Delal
dc.institutionauthorÖzerdem, Mehmet Siraç
dc.language.isoenen_US
dc.publisherDicle Üniversitesi Mühendislik Fakültesien_US
dc.relation.ispartofDicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCepstrum analysisen_US
dc.subjectClassificationen_US
dc.subjectEEGen_US
dc.subjectFocal anden_US
dc.subjectNon- focalen_US
dc.titleA classification approach for focal/non-focal EEG detection using cepstral analysisen_US
dc.titleA classification approach for focal/non-focal EEG detection using cepstral analysis
dc.typeArticleen_US

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