Various kinds of methods have been developed to detect the myocardial infarction (MI) which becomes one of the biggest causes of death. In this case, early detection of MI is necessary to be done. This article provided the completion of detection techniques for the MI. The detection of MI can be done by using 12 signals of the EKG by submitting entropy energy and morphological features. The initial stages may consist of data preprocessing to eliminate noise and down-sampling, pulse segmentation, data augmentation, and QRS detection. This stage was done to find the electrocardiographic features that can give feature of the incidence of MI. This feature was used for the input methods such as Support Vector Machine and convolutional neural networks (CNN). This study indicated that the detection done using SVM can show performance testing until 99.81%.
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27 December 2019
INTERNATIONAL CONFERENCE ON SCIENCE AND APPLIED SCIENCE (ICSAS) 2019
20 July 2019
Surakarta, Indonesia
Research Article|
December 27 2019
A review of methods for myocardial infarction detection using of electrocardiographic features
Dewi Cahya Fitri;
Dewi Cahya Fitri
a)
1
Physics Department of Graduate Program Sebelas Maret University
, Jl. Ir. Sutami 36A Kentingan Jebres Surakarta, Indonesia
a)Corresponding author: dewicahyafitri@yahoo.com
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Nuryani Nuryani;
Nuryani Nuryani
b)
1
Physics Department of Graduate Program Sebelas Maret University
, Jl. Ir. Sutami 36A Kentingan Jebres Surakarta, Indonesia
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Anton Satriyo Nugraha
Anton Satriyo Nugraha
2
Center for Information & Communication Technology, Agency for Assessment and Application of Technology
, Indonesia
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a)Corresponding author: dewicahyafitri@yahoo.com
AIP Conf. Proc. 2202, 020098 (2019)
Citation
Dewi Cahya Fitri, Nuryani Nuryani, Anton Satriyo Nugraha; A review of methods for myocardial infarction detection using of electrocardiographic features. AIP Conf. Proc. 27 December 2019; 2202 (1): 020098. https://doi.org/10.1063/1.5141711
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