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An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention.

Yanfang Dong^1,2, Miao Zhang², Lishen Qiu¹

¹School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China.

|June 28, 2023

Summary

A new deep learning model, CNN-DVIT, improves automatic arrhythmia detection from electrocardiograms (ECGs). This advanced method enhances cardiovascular disease diagnosis by accurately classifying arrhythmias in multi-lead ECG signals.

Keywords:

ECG signal arrhythmia deep learning deformable attention transformer depthwise separable convolution

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Area of Science:

Cardiology
Artificial Intelligence
Biomedical Engineering

Background:

Electrocardiograms (ECGs) are crucial for monitoring heart activity and diagnosing cardiovascular diseases (CVDs).
Automatic arrhythmia detection from ECGs is vital for early CVD prevention and diagnosis.
Current transformer-based deep learning models show limitations in multi-lead ECG arrhythmia detection.

Purpose of the Study:

To develop an advanced end-to-end multi-label arrhythmia classification model for 12-lead ECGs.
To improve the performance of deep learning models in detecting arrhythmias from varied-length ECG recordings.
To enhance computer-aided diagnosis technology for clinical ECG analysis.

Main Methods:

Proposed CNN-DVIT model combining convolutional neural networks (CNNs) with depthwise separable convolution and a vision transformer with deformable attention.
Incorporated a spatial pyramid pooling layer to handle varied-length ECG signals.
Evaluated the model on the CPSC-2018 dataset for multi-label arrhythmia classification.

Main Results:

Achieved an F1 score of 82.9% on the CPSC-2018 dataset.
CNN-DVIT outperformed existing transformer-based ECG classification algorithms.
Ablation studies confirmed the efficiency of deformable multi-head attention and depthwise separable convolution in feature extraction.

Conclusions:

The CNN-DVIT model demonstrates strong performance in automatic arrhythmia detection from multi-lead ECG signals.
This research offers significant support for clinical ECG analysis and arrhythmia diagnosis.
The findings contribute to the advancement of computer-aided diagnosis technology in cardiology.