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ECG data dependency for atrial fibrillation detection based on residual networks

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Abstract
Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detection algorithms have recently been developed. However, deep learning models were often trained with limited datasets and were evaluated within the same datasets, which makes their performance generally drops on the external datasets, known as data dependency. For this study, three different databases from PhysioNet were used to investigate the data dependency of deep learning-based AF detection algorithm using the residual neural network (Resnet). Resnet 18, 34, 50 and 152 model were trained with raw electrocardiogram (ECG) signal extracted from independent database. The highest accuracy was about 98-99% which is evaluation results of test dataset from the own database. On the other hand, the lowest accuracy was about 53-92% which was evaluation results of the external dataset extracted from different source. There are data dependency according to the train dataset and the test dataset. However, the data dependency decreased as a large amount of train data.
Author(s)
서효창오석김현빈주세경
Issued Date
2021
Type
Article
Keyword
Atrial fbrillation (AF)DependencyAI
DOI
10.1038/s41598-021-97308-1
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8931
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_0da4d25ba9034d199e05998b2199c177&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,ECG%20data%20dependency%20for%20atrial%20fibrillation%20detection%20based%20on%20residual%20networks&offset=0&pcAvailability=true
Publisher
SCIENTIFIC REPORTS
Location
영국
Language
한국어
ISSN
2045-2322
Citation Volume
11
Citation Number
18256
Citation Start Page
0
Citation End Page
0
Appears in Collections:
Engineering > Medical Engineering
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