NDLI: Classification of ECG signal with Support Vector Machine Method for Arrhythmia Detection

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Classification of ECG signal with Support Vector Machine Method for Arrhythmia Detection

Content Provider	Scilit
Author	Turnip, Arjon Rizqywan, M. Ilham Kusumandari, Dwi E. Turnip, Mardi Sihombing, Poltak
Copyright Year	2018
Description	Journal: Journal of Physics: Conference Series An electrocardiogram is a potential bioelectric record that occurs as a result of cardiac activity. QRS Detection with zero crossing calculation is one method that can precisely determine peak R of QRS wave as part of arrhythmia detection. In this paper, two experimental scheme (2 minutes duration with different activities: relaxed and, typing) were conducted. From the two experiments it were obtained: accuracy, sensitivity, and positive predictivity about 100% each for the first experiment and about 79%, 93%, 83% for the second experiment, respectively. Furthermore, the feature set of MIT-BIH arrhythmia using the support vector machine (SVM) method on the WEKA software is evaluated. By combining the available attributes on the WEKA algorithm, the result is constant since all classes of SVM goes to the normal class with average 88.49% accuracy.
Related Links	http://iopscience.iop.org/article/10.1088/1742-6596/970/1/012012/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/970/1/012012
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	970
Language	English
Publisher	IOP Publishing
Publisher Date	2018-03-19
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Hardware and Architecture Arrhythmia Detection Support Vector Machine
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy

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