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Journal Article Deep Representation Learning with Sample Generation and Augmented Attention Module for Imbalanced ECG Classification
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Authors
Muhammad Zubair, Sungpil Woo, Sunhwan Lim, Daeyoung Kim
Issue Date
2024-05
Citation
IEEE Journal of Biomedical and Health Informatics, v.28, no.5, pp.2461-2472
ISSN
2168-2194
Publisher
Institute of Electrical and Electronics Engineers Inc.
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/JBHI.2023.3325540
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.
KSP Keywords
Attention mechanism, Classification Performance, Deep representation learning, ECG classification, Focal point, Generalized representation, Healthcare applications, Heartbeat monitoring, Imbalanced data distribution, Learning methods, MIT-BIH arrhythmia database
This work is distributed under the term of Creative Commons License (CCL)
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CC BY NC ND