A Temporal Transformer-Based Fusion Framework for Morphological Arrhythmia Classification

Nafisa Anjum, Khaleda Akhter Sathi, Md Azad Hossain, M. Ali Akber Dewan

Research output: Contribution to journalJournal Articlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number68
JournalComputers
Volume12
Issue number3
DOIs
Publication statusPublished - Mar. 2023

Keywords

  • biomedical signal processing
  • classification
  • electrocardiogram
  • sequential model
  • transformer

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