An improved PPG denoising methodology based on EEMD and wavelet threshold

Sun, Linjuan; Jia, Yuheng

doi:10.1109/itaic49862.2020.9339069

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…Using this analysis, VMD faces the parameter selection problem. To make the fault signal of the scraper conveyor match the best decomposition effect to the maximum extent, it is necessary to optimize the value of α, K [30]. GA is a global random search method proposed by Professor Holland according to the evolution mechanism of natural species.…”

Section: Ga Optimizes Vmd Parametersmentioning

confidence: 99%

Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox

Wang,

Guo,

Zhao

et al. 2023

Sensors

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This paper introduces a fault diagnosis method for mine scraper conveyor gearbox gears using motor current signature analysis (MCSA). This approach solves problems related to gear fault characteristics that are affected by coal flow load and power frequency, which are difficult to extract efficiently. A fault diagnosis method is proposed based on variational mode decomposition (VMD)–Hilbert spectrum and ShuffleNet-V2. Firstly, the gear current signal is decomposed into a series of intrinsic mode functions (IMF) by using VMD, and the sensitive parameters of VMD are optimized by using a genetic algorithm (GA). The Sensitive IMF algorithm judges the modal function sensitive to fault information after VMD processing. By analyzing the local Hilbert instantaneous energy spectrum for fault-sensitive IMF, an accurate expression of signal energy changing with time is obtained to generate the local Hilbert immediate energy spectrum dataset of different fault gears. Finally, ShuffleNet-V2 is used to identify the gear fault state. The experimental results show that the accuracy of the ShuffleNet-V2 neural network is 91.66% after 778 s.

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Section: Ga Optimizes Vmd Parametersmentioning

confidence: 99%

Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox

Wang,

Guo,

Zhao

et al. 2023

Sensors

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“…In which, those model functions whose coefficients are bigger than 𝑧 th = 0.2 are considered as pure model functions, conversely, those less than 𝑧 th = 0.2 which are IMF 1 (𝑡), IMF 2 (𝑡), IMF 9 (𝑡) and 𝑟 9 (𝑡) are deemed as dominated by noises, and will undergo the WT denoise treatment. Thirdly, we calculate the main frequencies of the pure and denoised IMF 𝑛 (𝑡), as well as 𝑟 9 (𝑡), then remove the components not in the range of pulse signal which is believed in 0.5-20 Hz [21]. Finally, the purified components are superimposed to acquire the denoised PPG signal (𝑦(𝑡)).…”

Section: Decomposition and Reconstructionmentioning

confidence: 99%

“…EMD based methods are more elastic than WT and adaptive filtering, but for the discontinuous signal EMD decomposition can lead to modal aliasing. Aiming at this problem, some modified EMD methods are contributed by adding Gaussian white noise to the signal, such as ensemble EMD (EEMD) [21,22], complementary EEMD (CEEMD) [23] and complete EEMD with adaptive noise (CEEMDAN) [24,25]. EEMD and CEEMD directly add noise to the original signal, which can solve the problem of modal aliasing, but the added noise may affect the decomposed signal and produce the problem of noise residue, with a result of mutated reconstruction signal.…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, the signal is decomposed into a set of intrinsic mode functions (IMFs) and residue signal by CEEMDAN method, and the coefficients of IMFs and residue signal with the original signal are calculated to identify noise, which is filtered out by WT method. Thirdly, as the frequency of physiological signal is 0.5-20 Hz [21], IMF or residue signal with the frequency not in 0.5-20 Hz is removed in this work. Finally, a fresh PPG signal can be reconstructed by superposing the treated IMFs and residue signal.…”

Section: Introductionmentioning

confidence: 99%

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A method for portable PPG signal dynamic acquisition and denoise based on CEEMDAN and wavelet transform

Tian

Jia

2023

J. Inst.

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Photoplethysmography (PPG) is a non-invasive technology and widely used in medical monitoring. Nowadays, dynamic vital signs monitoring based on PPG technology is in emergence and shows great potential for commercialization, however, it is still challenged by massive noise evoked by respiration and muscle contraction. Herein, a portable PPG signal's dynamic acquisition and denoise system is constructed and applied to blood pressure (BP) estimation, in which an improved PPG denoise method based on complete ensemble empirical mode decomposition with adaptive noise and wavelet transform (CEEMDAN-WT), is described. Firstly, original PPG signal is measured by the proposed hardware, then CEEMDAN decomposes it into a group of components. Secondly, the noise dominated components are found by calculating the coefficients between the components and original signal, and removed by WT. Thirdly, fast Fourier transform is performed to remove the component whose dominant frequency exceeds 0.5–20 Hz. Fourthly, a fresh PPG signal is reconstructed and compared with the signal rebuilt by other methods, which proves that CEEMDAN-WT has higher signal-to-noise ratio and lower root mean square error. Last but not least, the reconstructed signal is applied to estimate systolic and diastolic BP, according to Windkessel model and aided by neural network algorithm. Overall, this work demonstrates the feasibility of the portable PPG dynamic acquisition and its application for dynamic vital signs monitoring, in which CEEMDAN-WT algorithm can effectively remove most of the noises in dynamic PPG signal. In conclusion, it demonstrates CEEMDAN-WT method can effectively remove noise from PPG signals in the state of motion, it may have a good potential for calculating other physiological indexes besides BP, and push PPG applications from professional medical to daily life.

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Improving the quality of pulse rate variability derived from wearable devices using adaptive, spectrum and nonlinear filtering

Prucnal,

Polak,

Kazienko

2025

Biomedical Signal Processing and Control

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An improved PPG denoising methodology based on EEMD and wavelet threshold

Cited by 8 publications

References 17 publications

Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox

Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox

A method for portable PPG signal dynamic acquisition and denoise based on CEEMDAN and wavelet transform

Improving the quality of pulse rate variability derived from wearable devices using adaptive, spectrum and nonlinear filtering

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