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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

References

F. Zennifa and H. K. Fitrilina, “Prototipe Alat Deteksi Dini dan mandiri penyakit Jantung Menggunakan Sistem Pakar VCIRS, Arduino dan Handphone Android,” Universitas Andalas, 2013.

H. Author, “Coronary Heart Disease/ Indonesian (Penyakit Jantung Koroner),” Smart Patient, 2018. [Online]. Available: https://www21.ha.org.hk/smartpatient/EM/MediaLibraries/EM/EMMedia/Coronary-Heart-Disease-Indonesian-201801.pdf?ext=.pdf. [Accessed: 10-Nov-2018].

H.-Y. Shin, J.-Y. Lee, J. Song, S. Lee, J. Lee, B. Lim, H. Kim, and S. Huh, “Cause-of-death statistics in the Republic of Korea, 2014,” J. Korean Med. Assoc., vol. 59, no. 3, pp. 221–232, 2016.

J. Halomoan, “Analisa Sinyal EKG dengan Metoda HRV (Heart Rate Variability)pada Domain WaktuAktivitas Berdiri dan Terlentang,” in Seminar Nasional Aplikasi Teknologi Informasi (SNATI), 2013, pp. 29–35.

M. N. Sidqi, “Sistem Cerdas Deteksi Suara Untuk Pengklasifikasian Penyakit Jantung Menggunakan Jaringan Saraf Tiruan,” UNY, 2018.

B. H. Centre, “Electrocardiography (EKG),” Bunda Heart Centre, 2019. [Online]. Available: http://www.bundaheartcentre.com/wp-content/uploads/2015/04/ECG_hart-normal.jpg.

H. Sulastomo, R. Kusumawati, Y. H. Suselo, N. Purwaningtyas, D. Indarto, S. A. Jusup, and R. Myrtha, Buku Manual Keterampilan Klinis Interpretasi Pemeriksaan Elektrokardiografi (EKG). Surakarta: Fakultas Kedokteran Universitas Sebelas Maret Surakarta, 2019.

A. Hermawan, Jaringan Syaraf Tiruan Teori dan Aplikasi. Yogyakarta: Penerbit Andi, 2006

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Biomedical Commons

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