Open AccessDesign Methods of Detecting Atrial Fibrillation Using the Recurrent Neural Network Algorithm on the Arduino AD8232 ECG Module
Author(s) -
Jonathan Eprilio Soaduon Simanjuntak,
Masayu Leylia Khodra,
Martin Clinton Tosima Manullang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/537/1/012022
Subject(s) - heartbeat , atrial fibrillation , arduino , computer science , stroke (engine) , artificial neural network , recurrent neural network , artificial intelligence , pattern recognition (psychology) , cardiology , medicine , engineering , embedded system , mechanical engineering , computer security
Atrial fibrillation (AF) is part of a type of heart disease characterized by a rhythmic irregular heartbeat. AF conditions that occur continuously can potentially cause a stroke for sufferers. The method of reading and detecting the possibility of AF is needed to prevent the risk of stroke due to AF. In this research, the Recurrent Neural Network (RNN) method is used in classifying electrocardiogram readings to obtain accuracy in the assessment of AF. The data information used in the study was obtained from physicians who were the bases of ECG result image data, and data information was also obtained by implementing directly through a simple and low-cost ECG using Arduino AD8232 to test user information directly related to AF conditions at the user’s heart. RNN method that is tested can obtain more accurate accuracy values in detecting AF heart rate abnormalities, and the Arduino AD8232 module can be a good ECG in reading low-cost but high-accuracy heart records.