Keywords
Electrocardiogram, Heart, AD8232 module, Arduino, Artificial Neural Network
Document Type
Article
Abstract
This article aims to describe the accuracy of signal processing using neural networks. The design of this final project hardware consists of Arduino Uno, AD8232 module and electrodes. ECG signals obtained from respondents were used as test data for normal ECG signals, while for abnormal class test data the data used were obtained from the research website, namely physionet with atrial fibrillation class. The design process in this system includes the process of data acquisition, training, feature extraction, testing and classification with artificial neural networks. Based on the results of the performance of this device to record ECG signals on respondents obtained normal ECG signals because the results of recorded ECG signals have a similarity in the PQRST wave with a predetermined target. This system can detect the classification of the heart by recognizing the statistical characteristics of the two signal classes and is trained using neural networks. Based on the testing process using an artificial neural network obtained an accuracy of 76.9%.
First Page
49
Last Page
55
Page Range
49-55
Issue
1
Volume
4
Digital Object Identifier (DOI)
10.21831/elinvo.v4i1.28242
Source
https://journal.uny.ac.id/index.php/elinvo/article/view/28242
Recommended Citation
M. Rifali and D. Irmawati, "Sistem Cerdas Deteksi Sinyal Elektrokardiogram (EKG) untuk Klasifikasi Jantung Normal dan Abnormal Menggunakan Jaringan Syaraf Tiruan (JST),", vol. 4, no. 1, pp. 49 - 55, Dec 2019.
The definitive version is available at https://doi.org/10.21831/elinvo.v4i1.28242
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