Classification of electrocardiogram (ECG) data using deep learning methods

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dc.authorscopusid57063298500
dc.authorscopusid57220810252
dc.authorscopusid57220810876
dc.authorscopusid56952927700
dc.contributor.authorBozyiğit, Fatma
dc.contributor.authorErdemir, Fatih
dc.contributor.authorŞahin, Murat
dc.contributor.authorKılınç, Deniz
dc.date.accessioned2022-02-15T16:57:26Z
dc.date.available2022-02-15T16:57:26Z
dc.date.issued2020
dc.departmentBakırçay Üniversitesien_US
dc.description4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025en_US
dc.description.abstractClassification is one of the most widely used techniques in healthcare, especially concerning diagnosing cardiac disorders. Arrhythmia is a disorder of the heartbeat rate or rhythm, which may occur sporadically in daily life. Electrocardiogram (ECG) is an important diagnostic tool for analysing cardiac tissues and structures. It includes information about the heart structure and the function of its electrical conduction system. Since manual analysis of heartbeat rate is time-consuming and prone to errors, automatic recognition of arrhythmias using ECG signals has become an increasingly popular research focus in recent years. Current ECG analysis systems in literature generally have implemented well known machine learning algorithms. Due to the advent of powerful parallel computing hardware and the big data technologies, deep learning has also become a widely preferred technique in healthcare applications. In our study, we use ECG data in MIT-BIH Arrhythmia Database to develop a Convolutional Neural Networks (CNN) which is a deep feed-forward neural network type. The parameter tuned/optimized version of the proposed algorithm on top of the reduced feature dimension is more efficient than state of the art in terms of accuracy. Finally, we also compare the results of the proposed algorithm with Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and we provide the corresponding results in related sections. © 2020 IEEE.en_US
dc.identifier.doi10.1109/ISMSIT50672.2020.9255000
dc.identifier.isbn9781728190907
dc.identifier.scopus2-s2.0-85097680498en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ISMSIT50672.2020.9255000
dc.identifier.urihttps://hdl.handle.net/20.500.14034/161
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.journal4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectcardiac arrhythmiaen_US
dc.subjectcardiac disordersen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectdeep learningen_US
dc.subjectElectrocardiogramen_US
dc.subjectGated Recurrent Uniten_US
dc.subjectLong Short-Term Memoryen_US
dc.subjectmachine learningen_US
dc.subjectRecurrent Neural Networksen_US
dc.titleClassification of electrocardiogram (ECG) data using deep learning methodsen_US
dc.typeConference Objecten_US

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