BROWSE
Detail
Journal Article
Deep Representation Learning with Sample Generation and Augmented Attention Module for Imbalanced ECG Classification
- 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
- 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)
(CC BY NC ND)
(CC BY NC ND)
