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

被引：9
|
作者：
Turnip, Arjon [1 ]
Rizqywan, M. Ilham [1 ]
Kusumandari, Dwi E. [1 ]
Turnip, Mardi [2 ]
Sihombing, Poltak [3 ]
机构：
[1] Indonesian Inst Sci, Tech Implementat Unit Instrumentat Dev, Jakarta, Indonesia
[2] Univ Prima Indonesia, Technol & Comp Sci, Medan, Indonesia
[3] Univ Sumatera Utara, Fac Comp Sci & Informat Technol, Medan, Indonesia
来源：
INTERNATIONAL CONFERENCE ON INNOVATION IN EDUCATION, SCIENCE AND CULTURE (ICIESC-2017) | 2018年 / 970卷
关键词：
D O I：
10.1088/1742-6596/970/1/012012
中图分类号：
G40 [教育学];
学科分类号：
040101 ; 120403 ;
摘要：
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.
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收藏
页数：8
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