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AuthorSamiee K.
AuthorKiranyaz, Mustafa Serkan
AuthorGabbouj M.
AuthorSaramaki T.
Available date2022-04-26T12:31:23Z
Publication Date2015
Publication NameExpert Systems with Applications
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.eswa.2015.05.002
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84930636361&doi=10.1016%2fj.eswa.2015.05.002&partnerID=40&md5=fc150e32b8aefeb5a09abcc796962f1d
URIhttp://hdl.handle.net/10576/30635
AbstractInterpretation of long-term Electroencephalography (EEG) records is a tiresome task for clinicians. This paper presents an efficient, low cost and novel approach for patient-specific classification of long-term epileptic EEG records. We aim to achieve this with the minimum supervision from the neurologist. To accomplish this objective, first a novel feature extraction method is proposed based on the mapping of EEG signals into two dimensional space, resulting into a texture image. The texture image is constructed by mapping and scaling EEG signals and their associated frequency sub-bands into the gray-level image domain. Image texture analysis using gray level co-occurrence matrix (GLCM) is then applied in order to extract multivariate features which are able to differentiate between seizure and seizure-free events. To evaluate the discriminative power of the proposed feature extraction method, a comparative study is performed, against other dedicated feature extraction methods. The comparative performance evaluations show that the proposed feature extraction method can outperform other state-of-art feature extraction methods with a low computational cost. With a training rate of 25%, the overall sensitivity of 70.19% and specificity of 97.74% are achieved in the classification of over 163 h of EEG records using support vector machine (SVM) classifiers with linear kernels and trained by the stochastic gradient descent (SGD) algorithm.
Languageen
PublisherElsevier Ltd
SubjectClassification (of information)
Electroencephalography
Electrophysiology
Extraction
Feature extraction
Image processing
Image texture
Mapping
Stochastic systems
Support vector machines
Textures
Time varying networks
CHB-MIT dataset
Epileptic seizures
Haralick
Stochastic gradient descent
Textural feature
Biomedical signal processing
TitleLong-term epileptic EEG classification via 2D mapping and textural features
TypeArticle
Pagination7175-7185
Issue Number20
Volume Number42
dc.accessType Abstract Only


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