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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2021

Open Access 01-12-2021 | Research

Constrained transformer network for ECG signal processing and arrhythmia classification

Authors: Chao Che, Peiliang Zhang, Min Zhu, Yue Qu, Bo Jin

Published in: BMC Medical Informatics and Decision Making | Issue 1/2021

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Abstract

Background

Heart disease diagnosis is a challenging task and it is important to explore useful information from the massive amount of electrocardiogram (ECG) records of patients. The high-precision diagnostic identification of ECG can save clinicians and cardiologists considerable time while helping reduce the possibility of misdiagnosis at the same time.Currently, some deep learning-based methods can effectively perform feature selection and classification prediction, reducing the consumption of manpower.

Methods

In this work, an end-to-end deep learning framework based on convolutional neural network (CNN) is proposed for ECG signal processing and arrhythmia classification. In the framework, a transformer network is embedded in CNN to capture the temporal information of ECG signals and a new link constraint is introduced to the loss function to enhance the classification ability of the embedding vector.

Results

To evaluate the proposed method, extensive experiments based on real-world data were conducted. Experimental results show that the proposed model achieve better performance than most baselines. The experiment results also proved that the transformer network pays more attention to the temporal continuity of the data and captures the hidden deep features of the data well. The link constraint strengthens the constraint on the embedded features and effectively suppresses the effect of data imbalance on the results.

Conclusions

In this paper, an end-to-end model is used to process ECG signal and classify arrhythmia. The model combine CNN and Transformer network to extract temporal information in ECG signal and is capable of performing arrhythmia classification with acceptable accuracy. The model can help cardiologists perform assisted diagnosis of heart disease and improve the efficiency of healthcare delivery.
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Metadata
Title
Constrained transformer network for ECG signal processing and arrhythmia classification
Authors
Chao Che
Peiliang Zhang
Min Zhu
Yue Qu
Bo Jin
Publication date
01-12-2021
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2021
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-021-01546-2

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