A new neural network with adaptive activation function for classification of ECG Arrhythmias

Basit Öğe Kaydı
dc.contributor.authorTezel, Guelay
dc.contributor.authorOezbay, Yueksel
dc.date.accessioned2020-03-26T17:16:56Z
dc.date.available2020-03-26T17:16:56Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description11th International Conference on Knowledge-Based Intelligent Informational and Engineering Systems/17th Italian Workshop on Neural Networks -- SEP 12-14, 2007 -- Vietri sul Mare, ITALYen_US
dc.description.abstractThis study presents a comparative study of the classification accuracy of ECG signals using a well-known neural network architecture named multilayered perception (MLP) with backpropagation training algorithm, and a new neural network with adaptive activation function (AAFNN) for classification of ECG arrhythmias. The ECG signals are taken from MIT-BIH ECG database, which are used to classify ten different arrhythmias for training. These are normal sinus rhythm, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter. For testing, the proposed structures were trained by backpropagation algorithm. Both of them tested using experimental ECG records of 10 patients (7 male and 3 female, average age is 33.8 +/- 16.4). The results show that neural network with adaptive activation function is more suitable for biomedical data like as ECG in the classification problems and training speed is much faster than neural network with fixed sigmoid activation functionen_US
dc.description.sponsorshipUniv Studi Milano, Second Univ Naples, Comune Vietri Mare, Comune Salerno, Reg Campania, Minist Riforme Innovaz nella P A, Ctr Reg Informat Commun Technolen_US
dc.identifier.endpage+en_US
dc.identifier.isbn978-3-540-74817-5
dc.identifier.issn0302-9743en_US
dc.identifier.issn1611-3349en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21182
dc.identifier.volume4692en_US
dc.identifier.wosWOS:000250338500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER-VERLAG BERLINen_US
dc.relation.ispartofKNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS: KES 2007 - WIRN 2007, PT I, PROCEEDINGSen_US
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectANNen_US
dc.subjectadaptive activation functionen_US
dc.subjectclassificationen_US
dc.subjectECGen_US
dc.subjectarrhythmiaen_US
dc.titleA new neural network with adaptive activation function for classification of ECG Arrhythmiasen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu