Next Article in Journal
A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis
Next Article in Special Issue
On the Computation of the Dispersion Diagram of Symmetric One-Dimensionally Periodic Structures
Previous Article in Journal
A Prototype of Speech Interface Based on the Google Cloud Platform to Access a Semantic Website
Previous Article in Special Issue
Spin-Orbital Momentum Decomposition and Helicity Exchange in a Set of Non-Null Knotted Electromagnetic Fields
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(7), 269; https://doi.org/10.3390/sym10070269

Denoising of Magnetocardiography Based on Improved Variational Mode Decomposition and Interval Thresholding Method

Department of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Received: 23 May 2018 / Revised: 5 July 2018 / Accepted: 5 July 2018 / Published: 9 July 2018
(This article belongs to the Special Issue Symmetry in Electromagnetism)
Full-Text   |   PDF [3221 KB, uploaded 10 July 2018]   |  

Abstract

Recently, magnetocardiography (MCG) has attracted increasing attention as a non-invasive and non-contact technique for detecting electrocardioelectric functions. However, the severe background noise makes it difficult to extract information. Variational Mode Decomposition (VMD), which is an entirely non-recursive model, is used to decompose the non-stationary signal into the intrinsic mode functions (IMFs). Traditional VMD algorithms cannot control the bandwidth of each IMF, whose quadratic penalty lacks adaptivity. As a result, baseline drift noise is still present or medical information is lost. In this paper, to overcome the unadaptable quadratic penalty problem, an improved VMD model via correlation coefficient and new update formulas are proposed to decompose MCG signals. To improve the denoising precision, this algorithm is combined with the interval threshold algorithm. First, the correlation coefficient is calculated, to determine quadratic penalty, in order to extract the first IMF made up of baseline drift. Then, the new update formulas derived from the variance that describes the noise level are used, to perform decomposition on the rest signal. Finally, the Interval thresholding algorithm is performed on each IMF. Theoretical analysis and experimental results show that this algorithm can effectively improve the output signal-to-noise ratio and has superior performance. View Full-Text
Keywords: magnetocardiography; quadratic penalty; variational mode decomposition; correlation coefficient; interval thresholding method magnetocardiography; quadratic penalty; variational mode decomposition; correlation coefficient; interval thresholding method
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Liao, Y.; He, C.; Guo, Q. Denoising of Magnetocardiography Based on Improved Variational Mode Decomposition and Interval Thresholding Method. Symmetry 2018, 10, 269.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top