Design and Implementation of Digital Cheybshev Type I filter using XSG for Noise Reduction in ECG Signal

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.

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Response to comment on 'AIRE-deficient patients harbor unique high-affinity disease-ameliorating autoantibodies'

eLife ◽

10.7554/elife.45826 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 1

Author(s):

Christina Hertel ◽

Dmytro Fishman ◽

Anna Lorenc ◽

Annamari Ranki ◽

Kai Krohn ◽

...

Keyword(s):

Type I Diabetes ◽

Cloning And Expression ◽

Type I ◽

T Cell Tolerance ◽

Cell Tolerance ◽

False Negatives ◽

High Affinity ◽

Design And Implementation ◽

Statistical Approaches

In 2016, we reported four substantial observations of APECED/APS1 patients, who are deficient in AIRE, a major regulator of central T cell tolerance (Meyer et al., 2016). Two of those observations have been challenged. Specifically, ‘private’ autoantibody reactivities shared by only a few patients but collectively targeting >1000 autoantigens have been attributed to false positives (Landegren, 2019). While acknowledging this risk, our study-design included follow-up validation, permitting us to adopt statistical approaches to also limit false negatives. Importantly, many such private specificities have now been validated by multiple, independent means including the autoantibodies’ molecular cloning and expression. Second, a significant correlation of antibody-mediated IFNα neutralization with an absence of disease in patients highly disposed to Type I diabetes has been challenged because of a claimed failure to replicate our findings (Landegren, 2019). However, flaws in design and implementation invalidate this challenge. Thus, our results present robust, insightful, independently validated depictions of APECED/APS1, that have spawned productive follow-up studies.

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