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dc.contributor.authorKarim, Ahmad M.
dc.contributor.authorGuzel, Mehmet S.
dc.contributor.authorTolun, Mehmet R.
dc.contributor.authorKaya, Hilal
dc.contributor.authorCelebi, Fatih V.
dc.date.accessioned2021-02-23T11:59:33Z
dc.date.available2021-02-23T11:59:33Z
dc.date.issued2019
dc.identifier.issn0208-5216en_US
dc.identifier.urihttp://hdl.handle.net/11727/5376
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.en_US
dc.language.isoengen_US
dc.relation.isversionof10.1016/j.bbe.2018.11.004en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEnergy spectral densityen_US
dc.subjectDeep auto-encoderen_US
dc.subjectDeep learningen_US
dc.subjectMedical waveform data processen_US
dc.titleA new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processingen_US
dc.typearticleen_US
dc.relation.journalBIOCYBERNETICS AND BIOMEDICAL ENGINEERINGen_US
dc.identifier.volume39en_US
dc.identifier.issue1en_US
dc.identifier.startpage148en_US
dc.identifier.endpage159en_US
dc.identifier.wos000462350100012en_US
dc.identifier.scopus2-s2.0-85057632413en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergien_US


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