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Detection of Ventricular Arrhythmia from ECG

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dc.contributor.author RATTAR, M. SAEED
dc.contributor.author SHAH, SYED M. SHEHRAM
dc.contributor.author CHOWDHRY, B.S.
dc.contributor.author SHAH, SYED M. ZAIGHAM ABBAS
dc.date.accessioned 2019-11-04T07:13:44Z
dc.date.available 2019-11-04T07:13:44Z
dc.date.issued 2016-01-01
dc.identifier.issn 2519-5409
dc.identifier.uri http://142.54.178.187:9060/xmlui/handle/123456789/824
dc.description.abstract The Ventricular Arrhythmias particularly Ventricular Tachycardia (VT) and Ventricular Flutter (VF) are life threatening arrhythmias and can lead to heart attacks if not detected and treated timely. In this paper, a method has been proposed that can differentiate between normal Electrocardiograms (ECGs) and two abnormal ECGs of VT and VF. The classification is performed by means of Artificial Neural Networks (ANN). Reflection coefficients of the Auto-Regressive Models of extractions from the ECG recordings are computed and used as features for input to the ANN. The ANN is trained using ECG samples that are characteristic of Non-diseased (normal), VT and VF. After suitable training and validation, the proposed algorithm has been found to have an accuracy of 100%, 97% and 94% for classification of Normal ECG, VF and VT respectively. en_US
dc.language.iso en_US en_US
dc.publisher PASTIC en_US
dc.subject PASTIC en_US
dc.subject Arrhythmia detection en_US
dc.subject classification en_US
dc.subject Electrocardiogram en_US
dc.subject AR Modeling en_US
dc.subject Artificial Neural Networks en_US
dc.title Detection of Ventricular Arrhythmia from ECG en_US
dc.type Article en_US


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