A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing

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dc.contributor.authorKarim, Ahmad M.
dc.contributor.authorGüzel, Mehmet S.
dc.contributor.authorTolun, Mehmet R.
dc.contributor.authorKaya, Hilal
dc.contributor.authorÇelebi, Fatih, V
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-16T09:15:10Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-16T09:15:10Z
dc.date.issued2019
dc.departmentMühendislik Fakültesi
dc.description.abstractThis paper proposes a new framework for medical data processing which is essentially designed based on deep autoencoder and energy spectral density (ESD) concepts. The main novelty of this framework is to incorporate ESD function as feature extractor into a unique deep sparse auto-encoders (DSAEs) architecture. This allows the proposed architecture to extract more qualified features in a shorter computational time compared with the conventional frameworks. In order to validate the performance of the proposed framework, it has been tested with a number of comprehensive medical waveform datasets with varying dimensionality, namely, Epilepsy Serious Detection, SPECTF Classification and Diagnosis of Cardiac Arrhythmias. Overall, the ESD function speeds up the deep auto-encoder processing time and increases the overall accuracy of the results which are compared to several studies in the literature and a promising agreement is achieved. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
dc.identifier.doi10.1016/j.bbe.2018.11.004
dc.identifier.endpage159en_US
dc.identifier.issn0208-5216
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage148en_US
dc.identifier.urihttps://doi.org/10.1016/j.bbe.2018.11.004
dc.identifier.urihttps://hdl.handle.net/20.500.12451/4309
dc.identifier.volume39en_US
dc.identifier.wosWOS:000462350100012
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Ltd.
dc.relation.ispartofBiocbernetics and Biomedical Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectEnergy Spectral Density
dc.subjectDeep Auto-Encoder
dc.subjectDeep Learning
dc.subjectMedical Waveform Data Process
dc.titleA new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing
dc.typeArticle

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