Frequency information enhanced half instance normalization network for denoising electrocardiograms

Abstract

Background

Electrocardiogram (ECG) is crucial in diagnosing and preventing heart diseases. However, its efficacy is compromised by the interference of the external environment, leading to potential misdiagnoses. Thus, it is crucial to remove the noise in ECGs. Recently, deep-learning based ECGs denoising approaches have achieved impressive performance, however, they only considered the time-domain information of ECGs.

Methods

In this work, we propose a Frequency Information Enhanced Half Instance Normalization Network (FIEHINet) which integrates knowledge of both time and frequency domains into a deep-learning model for ECG signal denoising. Two branches are employed to extract time and frequency features for noise eliminating, respectively. Then the ECG signals are reconstructed based on the fused features. Furthermore, masked signal training is introduced to improve the generalization ability.

Results

In order to evaluate the proposed method, ECGs used are chosen from five different databases. The proposed method for ECG signal denoising achieved Sum of Squared Distances scores of 3.95 ± 7.04, 2.04 ± 3.20, and 0.998 ± 1.579 for three kinds of noise intensities. Meanwhile, the classification experimental results of the processed dataset with the proposed method are 3.8 % higher in F1 score than the original dataset.

Conclusion

A model for removing mixed noises is successfully developed and tested.

Significance

This study presents an ECG denoising technique based on Half Instance Normalization, time–frequency information, and masked signal training, which can improve ECG interpretation and potentially reduce misdiagnoses in clinical practice.