DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zubair, Muhammad | ko |
dc.contributor.author | Woo, Sungpil | ko |
dc.contributor.author | Lim, Sunhwan | ko |
dc.contributor.author | Kim, Daeyoung | ko |
dc.date.accessioned | 2024-06-20T10:00:11Z | - |
dc.date.available | 2024-06-20T10:00:11Z | - |
dc.date.created | 2023-11-24 | - |
dc.date.created | 2023-11-24 | - |
dc.date.issued | 2024-05 | - |
dc.identifier.citation | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.28, no.5, pp.2461 - 2472 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | http://hdl.handle.net/10203/319900 | - |
dc.description.abstract | Developing an efficient heartbeat monitoring system has become a focal point in numerous healthcare applications. Specifically, in the last few years, heartbeat classification for arrhythmia detection has gained considerable interest from researchers. This paper presents a novel deep representation learning method for the efficient detection of arrhythmic beats. To mitigate the issues associated with the imbalanced data distribution, a novel re-sampling strategy is introduced. Unlike the existing oversampling methods, the proposed technique transforms majority-class samples into minority-class samples with a novel translation loss function. This approach assists the model in learning a more generalized representation of crucially important minority class samples. Moreover, by exploiting an auxiliary feature, an augmented attention module is designed that focuses on the most relevant and target-specific information. We adopted an inter-patient classification paradigm to evaluate the proposed method. The experimental results of this study on the MIT-BIH arrhythmia database clearly indicate that the proposed model with augmented attention mechanism and over-sampling strategy significantly learns a balanced deep representation and improves the classification performance of vital heartbeats. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Deep Representation Learning With Sample Generation and Augmented Attention Module for Imbalanced ECG Classification | - |
dc.type | Article | - |
dc.identifier.wosid | 001221547700062 | - |
dc.identifier.scopusid | 2-s2.0-85174830193 | - |
dc.type.rims | ART | - |
dc.citation.volume | 28 | - |
dc.citation.issue | 5 | - |
dc.citation.beginningpage | 2461 | - |
dc.citation.endingpage | 2472 | - |
dc.citation.publicationname | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | - |
dc.identifier.doi | 10.1109/JBHI.2023.3325540 | - |
dc.contributor.localauthor | Kim, Daeyoung | - |
dc.contributor.nonIdAuthor | Zubair, Muhammad | - |
dc.contributor.nonIdAuthor | Lim, Sunhwan | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Arrhythmia detection | - |
dc.subject.keywordAuthor | beat classification | - |
dc.subject.keywordAuthor | imbalanced learning | - |
dc.subject.keywordAuthor | remote health monitoring | - |
dc.subject.keywordPlus | CONVOLUTIONAL NEURAL-NETWORK | - |
dc.subject.keywordPlus | HEARTBEAT CLASSIFICATION | - |
dc.subject.keywordPlus | MORPHOLOGY | - |
dc.subject.keywordPlus | FEATURES | - |
dc.subject.keywordPlus | MODEL | - |
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