Adaptive noise canceller for magnetocardiography

Tiporlini, Valentina; Nguyen, Nghia Q.; Alameh, Kamal

doi:10.1109/honet.2011.6149770

Cited by 3 publications

(1 citation statement)

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“…If the signal and noise have separate bandwidths, one can use conventional frequency domain filtering [9] techniques. Adaptive filtering techniques [10] measure the noise level of the measurement signal channels by measuring the noise channels. However, these simple preprocessing methods have limited effectiveness.…”

Section: Introductionmentioning

confidence: 99%

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

Liao

Guo

2018

Symmetry

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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.

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Section: Introductionmentioning

confidence: 99%