Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171854
Title: ECGTransForm: empowering adaptive ECG arrhythmia classification framework with bidirectional transformer
Authors: Eldele, Emadeldeen
El-Ghaish, Hany
Keywords: Engineering::Bioengineering
Issue Date: 2024
Source: Eldele, E. & El-Ghaish, H. (2024). ECGTransForm: empowering adaptive ECG arrhythmia classification framework with bidirectional transformer. Biomedical Signal Processing and Control, 89, 105714-. https://dx.doi.org/10.1016/j.bspc.2023.105714
Journal: Biomedical Signal Processing and Control 
Abstract: Cardiac arrhythmias, deviations from the normal rhythmic beating of the heart, are subtle yet critical indicators of potential cardiac challenges. Efficiently diagnosing them requires intricate understanding and representation of both spatial and temporal features present in Electrocardiogram (ECG) signals. This paper introduces \abb, a deep learning framework tailored for ECG arrhythmia classification. By embedding a novel Bidirectional Transformer (BiTrans) mechanism, our model comprehensively captures temporal dependencies from both antecedent and subsequent contexts. This is further augmented with Multi-scale Convolutions and a Channel Recalibration Module, ensuring a robust spatial feature extraction across various granularities. We also introduce a Context-Aware Loss (CAL) that addresses the class imbalance challenge inherent in ECG datasets by dynamically adjusting weights based on class representation. Extensive experiments reveal that \abb outperforms contemporary models, proving its efficacy in extracting meaningful features for arrhythmia diagnosis. Our work offers a significant step towards enhancing the accuracy and efficiency of automated ECG-based cardiac diagnoses, with potential implications for broader cardiac care applications. The source code is available at https://github.com/emadeldeen24/ECGTransForm.
URI: https://hdl.handle.net/10356/171854
ISSN: 1746-8094
DOI: 10.1016/j.bspc.2023.105714
Schools: School of Computer Science and Engineering 
Rights: © 2023 Elsevier Ltd. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1016/j.bspc.2023.105714.
Fulltext Permission: embargo_20260407
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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