Improved noise resistance processing electrocardiosignals adaptive noise CEEMDAN as a method of solving the basic problems of the application of Hilbert-Huang transform

Altay, Yeldos A.; Kremlev, Artem

doi:10.1109/eiconrus.2017.7910632

Cited by 12 publications

(3 citation statements)

References 3 publications

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“…The Hilbert transform is applied to the resulting mode functions. When processing and analyzing real signals by this method, incorrect division into mode functions may occur due to the presence of noise of various nature in the studied signal 6,7 .…”

Section: Methodsmentioning

confidence: 99%

Comparison of the results of human pulse signal analysis using the wavelet transform and the Hilbert-Huang transform

Ompokov,

Boronoev

2023

29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

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This paper considers methods for the analysis of non-stationary processesthe wavelet transform and the Hilbert-Huang transform. A comparative analysis of the results of time-frequency analysis of the human pulse signal using these methods has been carried out. The wavelet image and the Hilbert spectrum of instantaneous frequencies are presented here. It is shown that the obtained results are in good agreement with each other. The Hilbert-Huang transform allows for a more accurate assessment of the dynamics of changes in the spectral components of the signal over time.

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Section: Methodsmentioning

confidence: 99%

Comparison of the results of human pulse signal analysis using the wavelet transform and the Hilbert-Huang transform

Ompokov,

Boronoev

2023

29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics

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“…However, the calculation burden associated with the decomposition process is large and the reconstructed signals tend to be incomplete. In contrast with these earlier EMD approaches, CEEMDAN [18] adaptively adds white noise to the original signal. This approach not only solves the problem of obtaining different numbers of IMFs after adding different white noise levels, but also reduces the calculation burden of the CEEMD process, and thereby obtaines relatively complete reconstructed signals.…”

Section: Ceemdan Algorithmmentioning

confidence: 99%

“…This paper introduced the cross-correlation method for selecting significant IMFs. The cross-correlation method is used in the field of signal processing to represent the similarity between two signals, where one is a known signal and the other an unknown signal, and the characteristics of the unknown signal are determined according to its similarities with known signals [18]. In general, the cross-correlation coefficient ρ between a PD signal f (t) and noise is very small.…”

Section: Effective Selection Of Significant Imfsmentioning

confidence: 99%

Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

et al. 2019

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While both periodic narrowband noise and white noise are significant sources of interference in the detection and localization of partial discharge (PD) signals in power cables, existing research has focused nearly exclusively on white noise suppression. This paper addresses this issue by proposing a new signal extraction method for effectively detecting random PD signals in power cables subject to complex noise environments involving both white noise and periodic narrowband noise. Firstly, the power cable signal was decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the periodic narrowband noise and frequency aliasing in the obtained signal components were suppressed using singular value decomposition. Then, signal components contributing significantly to the PD signal were determined according to the cross-correlation coefficient between each component and the original PD signal, and the PD signal was reconstructed solely from the obtained significant components. Finally, the wavelet packet threshold method was used to filter out residual white noise in the reconstructed PD signal. The performance of the proposed algorithm was demonstrated by its application to synthesized PD signals with complex noise environments composed of both Gaussian white noise and periodic narrowband noise. In addition, the time-varying kurtosis method was demonstrated to accurately determine the PD signal arrival time when applied to PD signals extracted by the proposed method from synthesized signals in complex noise environments with signal-to-noise ratio (SNR) values as low as −6 dB. When the SNR was reduced to −23 dB, the arrival time error of the PD signal was only one sampling point.

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Improving the efficiency of voice control robots based on adaptive procedures

Zhumasheva

Kyzdarbekova

Abdulkhairov

2017

2017 IEEE II International Conference on Control in Technical Systems (CTS)

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Improved noise resistance processing electrocardiosignals adaptive noise CEEMDAN as a method of solving the basic problems of the application of Hilbert-Huang transform

Cited by 12 publications

References 3 publications

Comparison of the results of human pulse signal analysis using the wavelet transform and the Hilbert-Huang transform

Comparison of the results of human pulse signal analysis using the wavelet transform and the Hilbert-Huang transform

Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

Improving the efficiency of voice control robots based on adaptive procedures

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