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Design Methods of Detecting Atrial Fibrillation Using the Recurrent Neural Network Algorithm on the Arduino AD8232 ECG Module
Simanjuntak J.E.S.a, Khodra M.L.b, Manullang M.C.T.a,b
a Department Informatics Engineering, Institut Teknologi Sumatera, Lampung Selatan, Indonesia
b School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 Published under licence by IOP Publishing Ltd.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.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Atrial fibrillation,Data informations,Design method,Heart disease,Heart rates,High-accuracy,Recurrent neural network (RNN),User information[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]Information on ECG data used in this study was obtained from PhysioNet. PhysioNet is a website that provides information on data collection related to physiologic signals, which are signal record data. Accuracy related to data from information contained in the physio net is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) which is a research body related to Biomedical.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1755-1315/537/1/012022[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]