Pulse rate estimation from forehead photoplethysmograph signal using RLS adaptive filtering with dynamical reference signal

Wan, Chunting; Chen, Dongyi; Yang, Juan

doi:10.1016/j.bspc.2021.103189

Cited by 10 publications

(14 citation statements)

References 28 publications

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“…MAs are probably the most common issue in PPG analysis [ 1 ]. With PPG sensors being extremely sensitive to even the slightest movement, and the MA spectrum overlapping with the HR spectrum, MA detection and removal has been a field of research in recent years, unfortunately with no satisfactory results [ 7 , 12 , 16 , 17 , 18 , 19 , 21 ]. This fact significantly complicates the task of maintaining consistently clean PPG signals, especially concerning endeavors such as estimating blood pressure from PPG data, which is a popular area of investigation [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

“…The second category consists of adaptive filtering methods such as the Wiener filter, least mean squares, and recursive least squares [ 12 , 16 , 19 ]. These methods require additional signals to be used as a reference for the noisy components to be removed [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, continuous health monitoring requires the constant supply of clean data to be further processed in order to extract health-related indices. Although some studies have attempted uninterrupted HR monitoring with low-intensity MA PPGs, deviant results and the further use of additional signal sources necessitate less-dependent and more efficient techniques [ 12 , 16 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

Vraka,

Zangróniz,

Quesada

et al. 2023

Sensors

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Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted signal often remains unexploited. This work introduces a novel method able to reconstruct noisy PPGs and facilitate uninterrupted health monitoring. The algorithm starts with spectral-based MA detection, followed by signal reconstruction by using the morphological and heart-rate variability information from the clean segments adjacent to noise. The algorithm was tested on (a) 30 noisy PPGs of a maximum 20 s noise duration and (b) 28 originally clean PPGs, after noise addition (2–120 s) (1) with and (2) without cancellation of the corresponding clean segment. Sampling frequency was 250 Hz after resampling. Noise detection was evaluated by means of accuracy, sensitivity, and specificity. For the evaluation of signal reconstruction, the heart-rate (HR) was compared via Pearson correlation (PC) and absolute error (a) between ECGs and reconstructed PPGs and (b) between original and reconstructed PPGs. Bland-Altman (BA) analysis for the differences in HR estimation on original and reconstructed segments of (b) was also performed. Noise detection accuracy was 90.91% for (a) and 99.38–100% for (b). For the PPG reconstruction, HR showed 99.31% correlation in (a) and >90% for all noise lengths in (b). Mean absolute error was 1.59 bpm for (a) and 1.26–1.82 bpm for (b). BA analysis indicated that, in most cases, 90% or more of the recordings fall within the confidence interval, regardless of the noise length. Optimal performance is achieved even for signals of noise up to 2 min, allowing for the utilization and further analysis of recordings that would otherwise be discarded. Thereby, the algorithm can be implemented in monitoring devices, assisting in uninterrupted health-tracking.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

Vraka,

Zangróniz,

Quesada

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…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. 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].…”

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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Wrist pulse signal acquisition and analysis for disease diagnosis: A review

Guo

Liao

et al. 2022

Computers in Biology and Medicine

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Pulse rate estimation from forehead photoplethysmograph signal using RLS adaptive filtering with dynamical reference signal

Cited by 10 publications

References 28 publications

A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring

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

Wrist pulse signal acquisition and analysis for disease diagnosis: A review

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