A Photoplethysmograph Signal Preprocess Method Based on Wavelet Transform

Shao, Shiliang; Wang, Ting; Wang, Lebing; Li, Sinan; Yao, Chen

doi:10.1109/yac53711.2021.9486662

Cited by 5 publications

(7 citation statements)

References 8 publications

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“…Recently, many algorithms are available for reducing noise in PPG signals, which encompass the Discrete Wavelet Transform Recursive Inversion (DWT-RI) [1] , Empirical Modal Decomposition (EMD), Discrete Wavelet Transform [2] , Wavelet Transform and Decomposition Reconstruction [3] , SG noise reduction methods [4] , and MA filtering [5] . While MA filtering is commonly utilized, it tends to excessively smooth signals, leading to the loss of crucial original information within the signals.…”

Section: Introductionmentioning

confidence: 99%

Joint discrete wavelet transform and improved Savitzky-Golay filtering for noise reduction of PPG signals

Wang,

Yang,

Fan

2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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The Photoplethysmography (PPG) stands as a fundamental measurement in wearable devices for human physiological parameters. This signal is susceptible to interference from external environmental factors during measurement of human physiological parameters. Prior research has addressed this issue predominantly through discrete wavelet transform (DWT) based approaches, yielding some success. However, the signals processed by these methods are often not smooth enough or have distortions, subsequently undermining confidence in the accuracy of derived physiological parameters. To mitigate this issue, this paper introduces a novel algorithm integrating DWT with Savitzky-Golay (SG) filtering to reduce noise on PPG signals. Specifically, the algorithm initially employs DWT on the original PPG signal for signal decomposition, and the preliminary denoising signals are subsequently obtained using soft thresholding on these decomposed signals. After that, a grid search algorithm is adopted to optimize the parameters of the SG filter, aiming to realize the smoothing of PPG signals. The final denoising PPG signal can be obtained from the output port of the SG filter. Finally, the algorithm combining DWT and MA filtering is compared with our proposed algorithm, and the experimental results show that the proposed method not only effectively reduces the noise interference, but also preserves the original features of the signal, which verifies the effectiveness and advancement of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Joint discrete wavelet transform and improved Savitzky-Golay filtering for noise reduction of PPG signals

Wang,

Yang,

Fan

2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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“…Both β and ξ are the most valuable normal mode interference spectral parameters in many practical applications. For example, β can be used to improve the beamforming performance [2] and localize the source [3,4]. ξ can be directly used for passive ranging of broadband source [5,6].…”

Section: Introductionmentioning

confidence: 99%

Statistical inversion of normal‐mode interference spectral parameter in ocean waveguide

Gao,

Zhu,

2023

Electronics Letters

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A statistical inversion method of estimating normal mode interference spectral parameters (including waveguide invariance and interference frequency) from one‐dimensional broadband sound intensity spectrum (1D BSIS) is presented. First, the interference phase of 1D BSIS is compensated by a non‐linear factor for each pair of unknown parameters in a prior parameter space. Second, the singular value decomposition is performed for a data matrix, which is constructed by the phase‐compensated BSIS. Then, a misfit function is defined based on both the maximum singular value and the corresponding singular vector of the data matrix. Finally, the posterior probability distributions of unknown parameters are analysed based on statistical inversion theory. This method is suitable for the situation using a single receiver and without any environmental information. Numerical simulations illustrate the validity of the presented method.

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“…So far, it has been accepted the methods to eliminate noise from PPG signal of the locomotor state can be grouped as three kinds which are based on adaptive filtering [13,14], wavelet transform (WT) [15]- [17] and empirical mode decomposition (EMD) [18,19]. Adaptive filtering depends on extra reference acceleration signals simultaneously measured by accelerometers during collecting PPG signal, [13,14] which increases the cost of the test equipment.…”

Section: Introductionmentioning

confidence: 99%

“…Adaptive filtering depends on extra reference acceleration signals simultaneously measured by accelerometers during collecting PPG signal, [13,14] which increases the cost of the test equipment. To get rid of the dependence on reference signal WT based approaches were proposed by providing wavelet base to decompose the signal into different wavelet coefficients and selecting an appropriate threshold value and method to obtain purified PPG signal, [15]- [17] it can have a good denoise effect [18] but may introduce phase distortions in PPG data [19]. EMD based methods are similar to WT based ones, with the difference in the base functions which are obtained from the extreme envelope of PPG signal itself, not artificially selected in WT based methods [20].…”

Section: Introductionmentioning

confidence: 99%

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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A Photoplethysmograph Signal Preprocess Method Based on Wavelet Transform

Cited by 5 publications

References 8 publications

Joint discrete wavelet transform and improved Savitzky-Golay filtering for noise reduction of PPG signals

Joint discrete wavelet transform and improved Savitzky-Golay filtering for noise reduction of PPG signals

Statistical inversion of normal‐mode interference spectral parameter in ocean waveguide

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

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