A New Approach to Detection of ECG Arrhythmias: Complex Discrete Wavelet Transform Based Complex Valued Artificial Neural Network

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dc.contributor.authorOezbay, Yueksel
dc.date.accessioned2020-03-26T17:37:43Z
dc.date.available2020-03-26T17:37:43Z
dc.date.issued2009
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis paper presents the new automated detection method for electrocardiogram (ECG) arrhythmias. The detection system is implemented with integration of complex valued feature extraction and classification parts. In feature extraction phase of proposed method, the feature values for each arrhythmia are extracted using complex discrete wavelet transform (CWT). The aim of using CWT is to compress data and to reduce training time of network without decreasing accuracy rate. Obtained complex valued features are used as input to the complex valued artificial neural network (CVANN) for classification of ECG arrhythmias. Ten types of the ECG arrhythmias used in this study were selected from MIT-BIH ECG Arrhythmias Database. Two different classification tasks were performed by the proposed method. In first classification task (CT-1), whether CWT-CVANN can distinguish ECG arrhythmia from normal sinus rhythm was examined one by one. For this purpose, nine classifiers were improved and executed in CT-1. Second classification task (CT-2) was to recognize ten different ECG arrhythmias by one complex valued classifier with ten outputs. Training and test sets were formed by mixing the arrhythmias in a certain order. Accuracy rates were obtained as 99.8% (averaged) and 99.2% for the first and second classification tasks, respectively. All arrhythmias in training and test phases were classified correctly for both of the classification tasks.en_US
dc.description.sponsorshipSelcuk University's Scientific Research ProjectsSelcuk Universityen_US
dc.description.sponsorshipThis work is supported by the Coordinatorship of Selcuk University's Scientific Research Projects.en_US
dc.identifier.doi10.1007/s10916-008-9205-1en_US
dc.identifier.endpage445en_US
dc.identifier.issn0148-5598en_US
dc.identifier.issn1573-689Xen_US
dc.identifier.issue6en_US
dc.identifier.pmid20052896en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage435en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s10916-008-9205-1
dc.identifier.urihttps://hdl.handle.net/20.500.12395/23213
dc.identifier.volume33en_US
dc.identifier.wosWOS:000270881900004en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofJOURNAL OF MEDICAL SYSTEMSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectComplex wavelet transformen_US
dc.subjectComplex valued ANNen_US
dc.subjectDetectionen_US
dc.subjectECGen_US
dc.subjectArrhythmiaen_US
dc.titleA New Approach to Detection of ECG Arrhythmias: Complex Discrete Wavelet Transform Based Complex Valued Artificial Neural Networken_US
dc.typeArticleen_US

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