Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection
DC Field | Value | Language |
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dc.contributor.author | Laghari, Asif Ali | - |
dc.contributor.author | Sun, Yanqiu | - |
dc.contributor.author | Alhussein, Musaed | - |
dc.contributor.author | Aurangzeb, Khursheed | - |
dc.contributor.author | Anwar, Muhammad Shahid | - |
dc.contributor.author | Rashid, Mamoon | - |
dc.date.accessioned | 2023-10-29T13:40:19Z | - |
dc.date.available | 2023-10-29T13:40:19Z | - |
dc.date.created | 2023-10-29 | - |
dc.date.issued | 2023-09 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/89477 | - |
dc.description.abstract | Atrial fibrillation easily leads to stroke, cerebral infarction and other complications, which will seriously harm the life and health of patients. Traditional deep learning methods have weak anti-interference and generalization ability. Therefore, we propose a new-fashioned deep residual-dense network via bidirectional recurrent neural network (RNN) model for atrial fibrillation detection. The combination of one-dimensional dense residual network and bidirectional RNN for atrial fibrillation detection simplifies the tedious feature extraction steps, and constructs the end-to-end neural network to achieve atrial fibrillation detection through data feature learning. Meanwhile, the attention mechanism is utilized to fuse the different features and extract the high-value information. The accuracy of the experimental results is 97.72%, the sensitivity and specificity are 93.09% and 98.71%, respectively compared with other methods. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | NATURE PORTFOLIO | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.title | Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 001067753600044 | - |
dc.identifier.doi | 10.1038/s41598-023-40343-x | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, v.13, no.1 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85171136518 | - |
dc.citation.title | SCIENTIFIC REPORTS | - |
dc.citation.volume | 13 | - |
dc.citation.number | 1 | - |
dc.contributor.affiliatedAuthor | Anwar, Muhammad Shahid | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | ECG | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | EXTRACTION | - |
dc.subject.keywordPlus | SIGNAL | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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