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

Liao, Y. Gene; He, Congcong; Guo, Qiang

doi:10.3390/sym10070269

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…A low frequency (0.3 Hz) sinusoidal signal is used to simulate the baseline drift. [25] The MCG signal is a synthetic signal, [26] which does not contain noise. Baseline drift u[n] of different amplitudes is added to the MCG signal s[n], and the input signal-to-noise ratio (SNR) is used to measure the magnitude of the baseline drift relative to the MCG signal.…”

Section: Baseline Drift Removalmentioning

confidence: 99%

Measurement of T wave in magnetocardiography using tunnel magnetoresistance sensor

Lu¹,

Ji²,

Yang³

2023

Chinese Phys. B

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Several critical clinical applications of Magnetocardiography (MCG) involve its T wave. The T wave’s accuracy directly affects the diagnostic accuracy of MCG for ischemic heart disease and arrhythmogenic. Tunnel magnetoresistance (TMR) attracts attention as a new MCG measurement technique. However, the T waves measured by TMR are often drowned in noise. The accuracy of T waves needs to be discussed to determine the clinical value of MCG measured by TMR. This study uses an improved empirical mode decomposition (EMD) algorithm and averaging to eliminate the noise in the MCG measured by TMR. The MCG measured by TMR are compared with MCG measured by the optically pumped magnetometer (OPM) to judge its accuracy. Using the MCG measured by OPM as a reference, the relative errors in time and amplitude of the T wave measured by TMR are 3.4% and 1.8%, respectively. This is the first demonstration that TMR can accurately measure the time and amplitude of MCG T waves. The ability to provide reliable T wave data illustrates the significant clinical application value of TMR in MCG measurement.

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Section: Baseline Drift Removalmentioning

confidence: 99%

Measurement of T wave in magnetocardiography using tunnel magnetoresistance sensor

Lu¹,

Ji²,

Yang³

2023

Chinese Phys. B

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“…Meanwhile, CEEMD decomposes signals recursively into IMFs and is suitable mainly for a multicomponent signal whose modes are well separated in the time-frequency plane. However, VMD is more robust and sensitive to sampling and noise [24], as it can handle a multicomponent signal whose mode is very close and can detect and distinguish small and large-scale fluctuations [30]. When VMD and CEEMD are combined with MFDFA, the two methods improved, as MFDFA classifies the IMFs; although, combining with VMD gives better results than CEEMD because of its good internal mechanism [31].…”

Section: Gnss Signal Decomposition With Vmdmentioning

confidence: 99%

Mitigation of Ionospheric Scintillation Effects on GNSS Signals with VMD-MFDFA

Ahmed

Marlia

et al. 2019

Remote Sensing

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Severe scintillations degrade the satellite signal intensity below the fade margin of satellite receivers thereby resulting in failure of communication, positioning, and navigational services. The performance of satellite receivers is obviously restricted by ionospheric scintillation effects, which may lead to signal degradation primarily due to the refraction, reflection, and scattering of radio signals. Thus, there is a need to develop an ionospheric scintillation detection and mitigation technique for robust satellite signal receivers. Hence, variational mode decomposition (VMD) is proposed. VMD addresses the problem of ionospheric scintillation effects on global navigation satellite system (GNSS) signals by extracting the noise from the radio signals in combination with multifractal detrended fluctuation analysis (MFDFA). MFDFA helps as a criterion designed to detect and distinguish the intrinsic mode functions (IMFs) into noisy (scintillated) and noise-free (non-scintillated) IMF signal components using the MFDFA threshold. The results of the proposed method are promising, reliable, and have the potential to mitigate ionospheric scintillation effects on both the synthetic (simulated) and real GNSS data obtained from Manado station (latitude 1.34° S and longitude 124.82° E), Indonesia. From the results, the effectiveness of VMD-MFDFA over complementary ensemble empirical mode decomposition with MFDFA (CEEMD-MFDFA) is an indication of better performance.

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“…Some modified EMD methods have been proposed to inhibit mode mixing; unfortunately, they are all empirical methods. To overcome the influence of the parameter selection problem in VMD, some modified VMD methods are put forward, which are suitable for analyzing different kinds of signals, such as magnetocardiography (MCG) [17] and bearing signals [18]. The application of VMD in underwater acoustic field is very limited.…”

Section: Introductionmentioning

confidence: 99%

The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy

Chen

et al. 2019

Energies

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The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode decomposition (IVMD). IVMD, as an improved method of variational mode decomposition (VMD), solved the problem of choosing decomposition layers for VMD by using a frequency-aided method. Furthermore, a novel method of feature extraction for S-RN, which combines IVMD and sample entropy (SE), is put forward in this paper. In this study, four types of S-RN signals are decomposed into a group of intrinsic mode functions (IMFs) by IVMD. Then, SEs of all IMFs are calculated. SEs are different in the maximum energy IMFs (EIMFs), thus, SE of the EIMF is seen as a novel feature for S-RN. To verify the effectiveness of the proposed method, a comparison has been conducted by comparing features of center frequency and SE of the EIMF by IVMD, empirical mode decomposition (EMD) and ensemble EMD (EEMD). The analysis results show that the feature of S-RN can be obtain efficiently and accurately by using the proposed method.

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

Cited by 8 publications

References 30 publications

Measurement of T wave in magnetocardiography using tunnel magnetoresistance sensor

Measurement of T wave in magnetocardiography using tunnel magnetoresistance sensor

Mitigation of Ionospheric Scintillation Effects on GNSS Signals with VMD-MFDFA

The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy

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