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An Approach for ECG QRS Detection Using Support Vector Machine

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dc.contributor.author San, Aye Myat
dc.date.accessioned 2019-07-25T07:59:54Z
dc.date.available 2019-07-25T07:59:54Z
dc.date.issued 2011-12-29
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1343
dc.description.abstract Electrocardiogram (ECG) signal provides useful information of the condition of the heart. Most automatic ECG diagnosis techniques require an accurate detection of the QRS complexes. Among all ECG components, QRS complex is the most significant feature. Entropy based method for the detection pf QRS complexes (cardiac beat) will be presented. Support Vector Machine (SVM) can be used as a classifier to delineate QRS and non QRS regions. The detection rate can strongly depend on the quality of training, data representation and the mathematical basis of the classifier. The classification performance of this automatic classifier is concerned with the detection of arrhythmia categories with six beat types. en_US
dc.language.iso en en_US
dc.publisher Sixth Local Conference on Parallel and Soft Computing en_US
dc.title An Approach for ECG QRS Detection Using Support Vector Machine en_US
dc.type Article en_US

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