Synthetic photoplethysmogram generation using two Gaussian functions

Tang, Qunfeng; Chen, Zhencheng; Ward, Rabab; Elgendi, Mohamed

doi:10.1038/s41598-020-69076-x

Cited by 40 publications

(21 citation statements)

References 26 publications

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“…This point also needs to be verified. Furthermore, the optimal number of sech-waves on a pulse contour needs to be investigated, since the optimal number of waves to be used for PDA may vary [18].…”

Section: Results Of Figuresmentioning

confidence: 99%

Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves

Iuchi

Takahashi

et al. 2022

Applied Sciences

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Photoplethysmographic (PPG) pulses contain information about cardiovascular parameters. In particular, blood pressure can be estimated using PPG pulse decomposition analysis, which assumes that a PPG pulse is composed of the original heart ejection blood wave and its reflections in arterial branchings. Among pulse decomposition wave functions that have been studied in the literature, Gaussian waves are the most successful ones. However, a more adequate pulse decomposition function could be found to improve blood pressure estimates. In this paper, we propose pulse decomposition analysis using hyperbolic secant (sech) waves and compare results with corresponding Gaussian wave decomposition. We analyze how the parameters of each of the two types of decomposition waves correlate with blood pressure. For this analysis, continuous blood pressure data and PPG data were acquired from ten healthy volunteers. The blood pressure of volunteers was varied by asking them to hold their breath for up to 60 s. The results suggested sech wave decomposition had higher accuracy in estimating blood pressure than the Gaussian function. Thus, sech wave decomposition should be considered as a more robust alternative to Gaussian wave pulse decomposition for blood pressure estimation models.

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Section: Results Of Figuresmentioning

confidence: 99%

Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves

Iuchi

Takahashi

et al. 2022

Applied Sciences

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“…If noise is required, users can generate PPGs and then add noise. The advantage of this toolbox is that it can generate PPGs with annotations; previous studies [15][16][17][18][19] only generated the PPGs without annotation. Thus, by using this toolbox, users can generate PPGs to test and compare the developed feature extraction algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…The beat-to-beat intervals were extracted from the RR interval in the electrocardiogram signal, and each PPG pulse was then connected according to the RR interval. Furthermore, the authors' previous work utilized a Two Gaussian Functions model based on circular motions to generate regular and irregular PPGs [18,19]. However, these methods [15][16][17][18][19] were focused on the morphology of the PPG, and no annotations were provided.…”

Section: Introductionmentioning

confidence: 99%

PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals

Tang

Chen

Menon

et al. 2021

Sensors

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An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.

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“…A. Signal simulation PPG signals were simulated using a modified version of the model proposed by Tang et al [12], [13]. In this model, each single PPG pulse is simulated as the summation of two Gaussian functions with different parameters according to the quality of the PPG signal to be simulated.…”

Section: Methodsmentioning

confidence: 99%

Outlier Management for Pulse Rate Variability Analysis from Photoplethysmographic Signals

Mejía-Mejía

Kyriacou

2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Pulse rate variability (PRV) has been proposed as a surrogate for the estimation of Heart Rate Variability (HRV), which is a non-invasive technique used to assess the cardiac autonomic activity. However, both physiological and technical factors may affect the relationship between HRV and PRV, and there are no standards for the analysis of PRV from photoplethysmographic (PPG) signals. The aim of this study was to determine the best outlier management strategies for PRV analysis. 117 PPG signals with randomly generated PRV information were simulated using Gaussian signals. From these, interbeat intervals were detected and different outlier detection and correction techniques were applied. Time and frequency-domain and non-linear PRV indices were extracted and compared with respect to the gold standard values obtained from the simulated PRV information. The results show that, in good quality PPG signals, there is no need to apply any outlier management technique for the extraction of PRV information.Clinical relevance-Establishing guidelines for PRV measurement can lead to more reliable and comparable results, as well as to the increase in the use of this variable for the diagnosis and monitoring of cardiovascular and autonomic conditions.

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Synthetic photoplethysmogram generation using two Gaussian functions

Cited by 40 publications

References 26 publications

Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves

Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves

PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals

Outlier Management for Pulse Rate Variability Analysis from Photoplethysmographic Signals

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