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A Temporal Transformer-Based Fusion Framework for Morphological Arrhythmia Classification

Authors :
Nafisa Anjum
Khaleda Akhter Sathi
Md. Azad Hossain
M. Ali Akber Dewan
Source :
Computers, Vol 12, Iss 3, p 68 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

By using computer-aided arrhythmia diagnosis tools, electrocardiogram (ECG) signal plays a vital role in lowering the fatality rate associated with cardiovascular diseases (CVDs) and providing information about the patient’s cardiac health to the specialist. Current advancements in deep-learning-based multivariate time series data analysis, such as ECG data classification include LSTM, Bi-LSTM, CNN, with Bi-LSTM, and other sequential networks. However, these networks often struggle to accurately determine the long-range dependencies among data instances, which can result in problems such as vanishing or exploding gradients for longer data sequences. To address these shortcomings of sequential models, a hybrid arrhythmia classification system using recurrence along with a self-attention mechanism is developed. This system utilizes convolutional layers as a part of representation learning, designed to capture the salient features of raw ECG data. Then, the latent embedded layer is fed to a self-attention-assisted transformer encoder model. Because the ECG data are highly influenced by absolute order, position, and proximity of time steps due to interdependent relationships among immediate neighbors, a component of recurrence using Bi-LSTM is added to the encoder model to address this characteristic of the data. The model performance indices such as classification accuracy and F1-score were found to be 99.2%. This indicates that the combination of recurrence along with self-attention-assisted architecture produces improved classification of arrhythmia from raw ECG signal when compared with the state-of-the-art models.

Details

Language :
English
ISSN :
2073431X
Volume :
12
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Computers
Publication Type :
Academic Journal
Accession number :
edsdoj.f861053bf1148b8829035e24f5e801d
Document Type :
article
Full Text :
https://doi.org/10.3390/computers12030068