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Cardiovascular disease is a major life-threatening condition commonly monitored through electrocardiogram (ECG) signals. However, the ECG signals currently generated by sensors are often accompanied by a plethora of diverse types of noise with different intensities, which causes a lot of interference in downstream tasks. In this work, we propose a deep learning based method for ECG signal denoising. Due to the different frequency characteristics of different types of noises, we use a Transformer with local enhancement as a feature extractor which can capture global dependencies. In addition, we introduce an R-wave attention mechanism to improve the most difficult R-wave reconstruction. Our experimental results demonstrate the effectiveness of our approach in denoising different types of strong noises, outperforming the state-of-the-art (SOTA) methods.
An implement of the RA-LENet.
You may need to download the data from these websites.
- MIT-BIH Arrhythmia Database
- MIT-BIH Noise Stress Test Database
- Lobachevsky University Electrocardiography Database
The python package you may need to be download and installed.
The other packages you can installed with pip:
- einops
- matplotlib
- numpy
- pandas
- scikit_learn
- scipy
- sktime
- torch
- tqdm
- wfdb
- Restructure the code.
- Package the code and environment into a docker image.
@INPROCEEDINGS{10650979,
author={Zhu, Yaolong and Zhu, Ding and Liu, Juan},
booktitle={2024 International Joint Conference on Neural Networks (IJCNN)},
title={RA-LENet:R-Wave Attention and Local Enhancement for Noise Reduction in ECG Signals},
year={2024},
volume={},
number={},
pages={1-9},
keywords={Noise;Noise reduction;Neural networks;Interference;Electrocardiography;Sensor phenomena and characterization;Feature extraction;ECG signal;denoising;Transformer},
doi={10.1109/IJCNN60899.2024.10650979}}