ECG Signal Denoising Method Based on Disentangled Autoencoder

The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. However, ECG data are usually disturbed by various types of noise, which can lead to errors in diagnosis by doctors. To address this problem, this study proposes a...

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Vydané v:Electronics (Basel) Ročník 12; číslo 7; s. 1606
Hlavní autori: Lin, Haicai, Liu, Ruixia, Liu, Zhaoyang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.04.2023
Predmet:
Algorithms
Artificial neural networks
Cardiovascular disease
Coding
Deep learning
Diagnosis
Electrocardiogram
Electrocardiography
Electrodes
Heart
Heart diseases
Methods
Neural networks
Noise
Noise reduction
Scientific validity
Signal processing
Signal to noise ratio
Wavelet transforms
China
ISSN:2079-9292, 2079-9292
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Shrnutí:The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. However, ECG data are usually disturbed by various types of noise, which can lead to errors in diagnosis by doctors. To address this problem, this study proposes a method for denoising ECG based on disentangled autoencoders. A disentangled autoencoder is an improved autoencoder suitable for denoising ECG data. In our proposed method, we use a disentangled autoencoder model based on a fully convolutional neural network to effectively separate the clean ECG data from the noise. Unlike conventional autoencoders, we disentangle the features of the coding hidden layer to separate the signal-coding features from the noise-coding features. We performed simulation experiments on the MIT-BIH Arrhythmia Database and found that the algorithm had better noise reduction results when dealing with four different types of noise. In particular, using our method, the average improved signal-to-noise ratios for the three noises in the MIT-BIH Noise Stress Test Database were 27.45 db for baseline wander, 25.72 db for muscle artefacts, and 29.91 db for electrode motion artefacts. Compared to a denoising autoencoder based on a fully convolutional neural network (FCN), the signal-to-noise ratio was improved by an average of 12.57%. We can conclude that the model has scientific validity. At the same time, our noise reduction method can effectively remove noise while preserving the important information conveyed by the original signal.
Bibliografia:ObjectType-Article-1
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12071606