FPGA-based design and implementation of arterial pulse wave generator using piecewise Gaussian–cosine fitting

Wang, Lu; Xu, Lisheng; Zhao, Dazhe; Xu, Lisong; Song, Dan

doi:10.1016/j.compbiomed.2015.02.005

Cited by 11 publications

(8 citation statements)

References 28 publications

(32 reference statements)

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“…The parameter α > 0 in Obj( P) plays the role of the penalty parameter for radial AI and maximum value of P, i.e., as α increases, the solution for (17) will minimize the difference of AI (and the maximum) value between P and P. In this study, the parameter α was chosen to be 40. When the parameter gradually increases starting at α = 40, given the minimizing problem (17), the difference between P's AI and reconstructed P's AI becomes smaller, while the L 2 -error between P and P becomes too large. On the other hands, if α was smaller than 40, AI index of the reconstructed P was not accurate.…”

Section: E Ppw Approximation Modelmentioning

confidence: 99%

“…} are approximations of the forward wave, the first reflected wave, and the second reflected wave of the PPW, respectively. Let us describe the fitting process to solve (17) for the target waveform P 65 (the process for the rest of age groups is similar). It is important to find reasonable initial guesses for the w (0) = (w…”

Section: E Ppw Approximation Modelmentioning

confidence: 99%

“…This parameter fitting process was divided into two steps to avoid non-physiological solutions. Firstly, (17) was solved with α = 0 using the iterative method based on a line search starting from w (0) , k (0) and w (0) , resulting the parameters w (1) , k (1) and t (1) . The obtained parameters w (1) , k (1) and t (1) are then used to determine the final parameters w (2) , k (2) and t (2) which are obtained by solving (17) with α = 40.…”

Section: E Ppw Approximation Modelmentioning

confidence: 99%

“…However, the signal processing algorithms are sensitive to the selection of parameters, and identifying optimal values is challenging. Other approaches include the use of several Gaussian functions to decompose aortic pulse pressure waveforms (PPWs) [15]- [17], and the wave reflection model analyses [18]- [20]. One of the oldest established theories, the wave reflection model analyses utilize the relationships between aortic PP and flow rate via the characteristic impedance and they are still used effectively.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Mathematical Model for Age-Dependent Radial Artery Pulse Wave Analysis Based on Pulse Waveform Decomposition

Yang

Kim

et al. 2020

IEEE Access

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Radial artery pulse waveforms (RAPWs) have been actively studied for decades because they provide critical health information, particularly related to risk factors of cardiovascular disease. The primary goal of this study is to develop a novel mathematical model that can regenerate age-dependent RAPWs by decomposing a single pulse pressure waveform (PPW). This study proposes to decompose a PPW into three waveform components with one forward wave component and two reflected wave components based on the physiology of pulse waveforms. Treated as basis functions, the three components with associated control parameters are incorporated in the proposed mathematical model. The underlying idea of the model is to generate desired pulse waveforms by combining basis functions whose characteristics depend on selection of control parameter values. For the current study, after determining the nine control parameters of the basis functions by post-processing algorithms, the proposed model for the PPW is derived from a linear combination of the basis functions. Using the model along with in vivo RAPW data, this study evaluates the performance of the model in regenerating PPWs for a wide range of ages from 85 years old to 15 years old. The results show an error of less than 6% between the PPWs numerically derived from the model and the in vivo data for the two key matrixes used in this study (the least-square error norm and the radial augmentation index), validating the effectiveness of the model. The model is applicable for RAPW simulator analysis and development. INDEX TERMS Age-dependent, hemodynamic, radial artery pulse waveform, wave decomposition, wave reflection model.

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Section: E Ppw Approximation Modelmentioning

confidence: 99%

Section: E Ppw Approximation Modelmentioning

confidence: 99%

Section: E Ppw Approximation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of a Mathematical Model for Age-Dependent Radial Artery Pulse Wave Analysis Based on Pulse Waveform Decomposition

Yang

Kim

et al. 2020

IEEE Access

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“…Recently, the optimal expression of the morphological model has drawn much attention, and the related works focus on reducing the error between kernel function mixture and measured ABP waveform by selecting the optimal number or/and type of kernel function. The Gaussian, Lognormal, Rayleigh, Gamma [16], Double-exponential [17] and Cosine [18] functions have been employed as the kernel functions of the morphological models and their number have been tried from two to seven [12]. Sološenko et al [19] concluded that the model consists of one-Lognormal and two-Gaussian functions mixture that has the minimum error for the data of atrial fibrillation after comparing the four models with different kernel function mixtures.…”

Section: Introductionmentioning

confidence: 99%

The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures

2020

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The morphological modeling methods are efficient in quantifying the change of arterial blood pressure (ABP) waves. The related works focus on minimizing the modeling error but ignore the classification related modeling expression in practical applications. In this study, we explored the optimal modeling method for ABP wave related classifications. Two types of conventional models, Gaussian or Lognormal kernel function mixtures, were employed to quantitively describe the change of ABP signals, and the parameters of different models were engaged to train the different classifiers by probabilistic neural network (PNN) and random forest (RF) for identifying the ABP waves by age, gender, and whether belonging to extreme bradycardia (EB) or extreme tachycardia (ET). Then, we defined some indexes about the performance of modeling and classifications as the references to compare the different models. The ABP signals of Fantasia and 2015 PhysioNet/CinC Challenge databases were exploited as the experimental data to select the optimal model. The modeling results show that the Lognormal kernel function mixtures have a lower error in ABP wave modeling. The two-sample Kolmogorov-Smirnov test (ks-test) results indicate that the parameters of all models are markedly different at a highly significant level (h = 1, p < 0.05) between different groups. The classification results show that the classifiers based on the four-Gaussian function model have the best performance with the average Kappa coefficients (KC) of 99.160 ± 0.123%, while the average KC for the classifiers of two-Lognormal function models is 97.585 ± 0.172%, which means there is excessive information redundancy in the classifications by the three and four kernel functions models. Considering some other indexes such as time consumption and RAM space, the 2 Lognormal function model has more potential in practical applications. INDEX TERMS Arterial blood pressure signal, optimal morphological model, Gaussian function, lognormal function, classification.

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Ultrafast Self‐Healing and Injectable Conductive Hydrogel for Strain and Pressure Sensors

et al. 2019

Adv Materials Technologies

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The in vivo detection of body motion and subtle physiological signals, including respiration and sphygmus waves, remains a major challenge. High‐precision, miniature sensors are urgently needed to investigate physiological responses against particular stimuli. Injectable conductive self‐healing (ICSH) hydrogels are promising candidates for flexible wearable electronics in healthcare monitoring, owing to their mechanical and electrical characteristics. Given this, an interpenetrating polymer network (IPN) of multiwalled carbon nanotube‐poly(3,4‐ethylenedioxythiophene)/poly(styrenesulfonate)‐polyacrylamide‐poly(vinyl alcohol)/borax composite hydrogel (CNT‐PEDOT‐PAM‐PVA) is cross‐linked in a simplified process to achieve reliable physiological signal detection. The CNT‐PEDOT‐PAM‐PVA IPN offers ultra‐low Young's modulus, high stretchability, rapid self‐healing, and injectability. A miniature strain sensor fabricated via material injection shows consistent and linear responses to mechanical stimuli. It can distinguish between the respiration amplitude and frequency of a mouse during different activities. Pressure micro‐sensors fabricated through bottom‐up printing–injecting can successfully monitor precise sphygmus signals from the human radial and carotid arteries. The feasibility and advantages of ICSH hydrogels in the detection of physiological signals in vivo are demonstrated. Such miniature sensors can contribute to healthcare monitoring and the understanding of the biological basis of a particular behavior or neuropsychiatric disorder.

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FPGA-based design and implementation of arterial pulse wave generator using piecewise Gaussian–cosine fitting

Cited by 11 publications

References 28 publications

Development of a Mathematical Model for Age-Dependent Radial Artery Pulse Wave Analysis Based on Pulse Waveform Decomposition

Development of a Mathematical Model for Age-Dependent Radial Artery Pulse Wave Analysis Based on Pulse Waveform Decomposition

The Optimal Morphological Model for Arterial Blood Pressure Wave Related Classification: Comparison of Two Types of Kernel Function Mixtures

Ultrafast Self‐Healing and Injectable Conductive Hydrogel for Strain and Pressure Sensors

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