Adjustable 60Hz noise reduction and ECG signal amplification of a remote electrocardiogram system

2004 ◽  
Author(s):  
Ying-Wen Bai ◽  
Chien-Yung Cheng ◽  
Cheng-Kai Lu ◽  
Chuang-Hsiang Huang ◽  
Yuh-Ting Chen ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Signal Amplification ◽  
Ecg Signal

scholarly journals An ECG Denoising Method Based on the Generative Adversarial Residual Network

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Bingxin Xu ◽  
Ruixia Liu ◽  
Minglei Shu ◽  
Xiaoyi Shang ◽  
Yinglong Wang
Keyword(s):  
Noise Reduction ◽  
Reduction Process ◽  
Experimental Results ◽  
Depth Information ◽  
Ecg Signal ◽  
Massachusetts Institute Of Technology ◽  
Residual Network ◽  
Denoising Autoencoder ◽  
Generative Adversarial Network ◽  
Ecg Denoising

High-quality and high-fidelity removal of noise in the Electrocardiogram (ECG) signal is of great significance to the auxiliary diagnosis of ECG diseases. In view of the single function of traditional denoising methods and the insufficient performance of signal details after denoising, a new method of ECG denoising based on the combination of the Generative Adversarial Network (GAN) and Residual Network is proposed. The method adopted in this paper is based on the GAN structure, and it restructures the generator and discriminator. In the generator network, residual blocks and Skip-Connecting are used to deepen the network structure and better capture the in-depth information in the ECG signal. In the discriminator network, the ResNet framework is used. In order to optimize the noise reduction process and solve the lack of local relevance considering the global ECG problem, the differential function and overall function of the maximum local difference are added in the loss function in this paper. The experimental results prove that the method used in this article has better performance than the current excellent S-Transform (S-T) algorithm, Wavelet Transform (WT) algorithm, Stacked Denoising Autoencoder (S-DAE) algorithm, and Improved Denoising Autoencoder (I-DAE) algorithm. Experiments show that the Root Mean Square Error (RMSE) of this method in the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) noise pressure database is 0.0102, and the Signal-to-Noise Ratio (SNR) is 40.8526 dB, which is compared with that of the most advanced experimental methods. Our method improves the SNR by 88.57% on average. Besides the three noise intensities for comparison experiments, additional noise reduction experiments are also performed under four noise intensities in our paper. The experimental results verify the scientific nature of the model, which is that our method can effectively retain the important information conveyed by the original signal.

Noise Reduction and Delineation of ECG Signal

2018 ◽  
Author(s):  
Jose Luis Ramirez Gonzalez ◽  
Jackeline Abad Torres ◽  
Oscar Camacho Quintero
Keyword(s):  
Noise Reduction ◽  
Ecg Signal

scholarly journals A two-stage ECG signal denoising method based on deep convolutional network

2020 ◽  
Author(s):  
Lishen Qiu ◽  
Wenqiang Cai ◽  
Jie Yu ◽  
Jun Zhong ◽  
Yan Wang ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Stress Test ◽  
Noise Removal ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Two Stage ◽  
Convolutional Network ◽  
Denoising Method ◽  
One Dimensional ◽  
Ecg Signals

AbstractElectrocardiogram (ECG) is an effective and non-invasive indicator for the detection and prevention of arrhythmia. ECG signals are susceptible to noise contamination, which can lead to errors in ECG interpretation. Therefore, ECG pretreatment is important for accurate analysis. In this paper, a method of noise reduction based on deep learning is proposed. The method is divided into two stages, and two corresponding models are formed. In the first stage, a one-dimensional U-net model is designed for ECG signal denoising to eliminate noise as much as possible. The one-dimensional DR-net model in the second stage is used to reconstruct the ECG signal and to correct the waveform distortion caused by noise removal in the first stage. In this paper, the U-net and the DR-net are constructed by the convolution method to achieve end-to-end mapping from noisy ECG signals to clean ECG signals. The ECG data used in this paper are from CPSC2018, and the noise signal is from MIT-BIH Noise Stress Test Database (NSTDB). In the experiment, the improvement in the signal-to-noise ratio SNRimp, the root mean square error decrease RMSEde, and the correlation coefficient P, are used to evaluate the performance of the network. This two-stage method is compared with FCN and U-net alone. The experimental results show that the two-stage noise reduction method can eliminate complex noise in the ECG signal while retaining the characteristic shape of the ECG signal. According to the results, we believe that the proposed method has a good application prospect in clinical practice.

Noise Reduction of ECG Signal Based on Morphological Filtering and WT

2010 ◽  
Vol 439-440 ◽  
pp. 12-16 ◽  
Author(s):  
Dan Zhang ◽  
Wen Tao Sui
Keyword(s):  
Wavelet Transform ◽  
Noise Reduction ◽  
High Frequency ◽  
Ecg Signal ◽  
Interference Signal ◽  
Morphological Filters ◽  
Morphological Filtering ◽  
Wavelet Algorithm

In order to eliminate the noise in ECG signal and increase the diagnosis efficiency, a method based on morphological filtering and wavelet algorithm is proposed. The morphological filters is used to filter out the baseline interference signal, and the wavelet transform is applied to remove high frequency interference. The experiment proves that the algorithm is effective.

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