Results 251 to 260 of about 36,871 (305)

Multi-Feature Adaptive Variational Mode Decomposition for Wearable ECG Devices. [PDF]

Biosensors (Basel)
Chen Z, Wu D, Nie Y, Zhang J, Liu G, He F, Mo L, Peng L, Zeng C, Liu Z.   +9 more
europepmc   +1 more source

KAN-DeScoD: Kolmogorov-Arnold Network Enhanced Deep Score-Based Diffusion Model for ECG Denoising. [PDF]

Sensors (Basel)
Shu Z, Zhai D, Huang L, Zhang Y, Liu T.
europepmc   +1 more source

An Effective LFM Signal Reconstruction Method for Signal Denoising

Journal of Circuits, Systems and Computers, 2018
One of the main challenges in signal denoising is to accurately restore useful signals in low signal-to-noise ratio (SNR) scenarios. In this paper, we investigate the signal denoising problem for multi-component linear frequency modulated (LFM) signals. An effective time-frequency (TF) analysis-based approach is proposed.
Luo, Shan, Bi, Guoan, Wu, Tong, Xiao, Yong, Lin, Rongping   +4 more
openaire   +3 more sources

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Bioacoustic signal denoising: a review

Artificial Intelligence Review, 2020
Animal biodiversity has been experiencing rapid decline due to various reasons such as habitat loss and degradation, invasive species, and environment pollution. Recent advances in acoustic sensors provide a novel way to monitor animals through investigating collected bioacoustic recordings.
Jie Xie 0001, Juan Gabriel Colonna, Jinglan Zhang   +2 more
openaire   +2 more sources

Signal discrimination without denoising

Communications in Statistics - Simulation and Computation, 2019
This article reveals that two previously established tests for autocovariance equality between stationary autoregressive moving average (ARMA) processes can also be used to discriminate between har...
Ferebee Tunno, Miranda Perry
openaire   +1 more source

Noise variance in signal denoising

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004
In the thresholding method of denoising the optimum threshold is obtained as a function of additive noise variance. In practical problems, where the variance of the noise is unknown, the first step is to estimate the noise variance. The estimated noise variance is then implemented in calculation of the optimum threshold.
Soosan Beheshti, Munther A. Dahleh
openaire   +1 more source

Denoising Ventricular tachyarrhythmia Signal

2018 9th International Symposium on Signal, Image, Video and Communications (ISIVC), 2018
Electrocardiogram signals (ECG) are among the most important sources of diagnostic information in healthcare. During ECG measurement, there may be various noises such as muscle contraction (electromyography), baselines wander, and power-line interferences, which interfered with the ECG information identification that causing a misinterpretation of the ...
Azdine Dliou, Samir Elouaham, Rachid Latif, Mustapha Laaboubi, Hicham Zougagh, Amine Saddik   +5 more
openaire   +1 more source

Signal denoising with average sampling

Digital Signal Processing, 2012
Based on the theory of average sampling, we present a new algorithm to reconstruct bandlimited signals from sampled values in the presence of zero mean, independent and identically distributed random noises. Numerical results show that our algorithm has good performance on denoising.
Qingyue Zhang, Ling Wang, Wenchang Sun
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Wavelet denoising for signals in quadrature

Integrated Computer-Aided Engineering, 2005
The idea of forming a complex-valued (analytic) signal from a real-valued one by creating an imaginary part equal to the Hilbert transform of the real part is well known in exploration geophysics for seismic character mapping via instantaneous attributes.
Sofia C. Olhede, Andrew T. Walden
openaire   +1 more source

An accelerated algorithm for ECG signal denoising

2022 16th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2022
The Electrocardiogram (ECG) signal is an important tool for cardiovascular diseases analysis. However, still today acquisition devices produce noisy signals that degrades the quality of information by corrupting important features. To improve the quality of the acquired data a filtering process is mandatory.
De Luca P., Galletti A., Marcellino L.
openaire   +1 more source