Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform

Wu, Hau‐Tieng; Wu, Han Kuei; Wang, Chun Li; Yang, Yueh Lung; Wu, Wen-Lan; Tsai, Tung Hu; Chang, Hen Hong

doi:10.1371/journal.pone.0157135

Cited by 17 publications

(11 citation statements)

References 51 publications

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“…Generally, the most common way to evaluate the hemodynamics in a vessel is to study the pulse wave signal (Fig. 3a) recorded at different locations (the most common from the arm, recording brachial pressure by the sphygmomanometer) 23 . As a matter of fact, this signal is useful for recording information about the blood pressure and the analysis of its waveform provides important clinical information.…”

Section: Resultsmentioning

confidence: 99%

“…The corresponding curve is characterized by the presence of two peaks, related to systole (SP) and diastole (DP), respectively, and a dicrotic notch (DN) between them. A distortion in the wave profile and, in particular, a variation of amplitude and position of these parameters are significant signs of an undesired variation in the patient hemodynamics, such as atherosclerosis or different heart dysfunctions 23 . In this respect, recording specific alterations of these signals on vascular grafts allows retrieving information on the graft status and highly predictive signs of prosthetic failures.…”

Section: Resultsmentioning

confidence: 99%

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Soft and flexible piezoelectric smart patch for vascular graft monitoring based on Aluminum Nitride thin film

Natta

Mastronardi

Guido

et al. 2019

Sci Rep

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Vascular grafts are artificial conduits properly designed to substitute a diseased blood vessel. However prosthetic fail can occur without premonitory symptoms. Continuous monitoring of the system can provide useful information not only to extend the graft’s life but also to optimize the patient’s therapy. In this respect, various techniques have been used, but all of them affect the mechanical properties of the artificial vessel. To overcome these drawbacks, an ultrathin and flexible smart patch based on piezoelectric Aluminum Nitride (AlN) integrated on the extraluminal surface of the prosthesis is presented. The sensor can be conformally wrapped around the external surface of the prosthesis. Its design, mechanical properties and dimensions are properly characterized and optimized in order to maximize performances and to avoid any interference with the graft structure during its activity. The sensorized graft is tested in vitro using a pulsatile recirculating flow system that mimics the physiological and pathological blood flow conditions. In this way, the ability of the device to measure real-time variations of the hemodynamics parameters has been tested. The obtained high sensitivity of 0.012 V Pa −1 m −2 , joint to the inherent biocompatibility and non-toxicity of the used materials, demonstrates that the device can successfully monitor the prosthesis functioning under different conditions, opening new perspectives for real-time vascular graft surveillance.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Soft and flexible piezoelectric smart patch for vascular graft monitoring based on Aluminum Nitride thin film

Natta

Mastronardi

Guido

et al. 2019

Sci Rep

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“…The harmonic three-phase model [11] reflects the waveform of the signal without taking into account its frequency and random component. Another option for pulse signal simulation is adaptive non-harmonic model [12], which does not take into account signal frequency. In addition to these shortcomings, the analysis of simulation models showed that they do not take into account changes in time and amplitude indicators due to the course of periodic medium-and long-term processes.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of a simulation model of a photoplethysmographic signal under psychoemotional stress

Yavorska

Strembitska

Strembitskyi

et al. 2021

EEJET

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A simulation model of a photoplethysmographic signal under psychoemotional stress taking into account the nature of signals of biological origin and stress response stages was developed. The method of constructing the simulation model is based on reconstructing the waveform and coding points of the signal taking into account the stress response curve using harmonic functions at characteristic time intervals. Using the simulation model of the photoplethysmographic signal under psychoemotional stress with previously known parameters allows validation of methods and algorithms for processing such data. It was found that in the process of simulation, it is necessary to take into account the signal frequency, random component and stress response curve. This complicates the simulation algorithm. However, using the simulation model with variable input parameters allows reproducing the signal with an emphasis on stress response stages. One of the features of the proposed model is the ability to reproduce the signal by coding points for amplitude and time intervals using harmonic functions. The relative error for the amplitude variation of the model and experimental data is 3.97 %, and for the period – 3.41 %. Calculation of Student's t-test showed a statistically insignificant difference: p=0.296 for the amplitude and p=0.275 for the period. This indicates that the simulation model takes into account the signal characteristics under stress: frequency, random component and stress response curve. Using the proposed simulation model is an adequate way to assess methods and algorithms for analyzing the state of the cardiovascular system under psychoemotional stress

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“…Among various challenges, one shared by many real-world signals is that the amplitude and frequency of each oscillatory component is time-varying, and the oscillatory pattern is usually not sinusoidal. With the advance of sensor technology, examples can be found in various areas, such as biomedicine [1][2][3], physics [4,5], to name but a few.…”

Section: Introductionmentioning

confidence: 99%

Decomposing non-stationary signals with time-varying wave-shape functions

Colominas,

2020

Preprint

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Modern time series are usually composed of multiple oscillatory components, with time-varying frequency and amplitude. The signal processing mission is further challenged if each component has oscillatory pattern far from a sinusoidal function. In biomedical field, the oscillatory pattern is even changing from time to time and contaminated by noise. In practice, if multiple components exist, it is desirable to robustly decompose the signal into each component for various purposes, and extract desired information. Such challenge have raised a significant amount of interest in the past decade, but a satisfactory solution is still lacking. We propose a novel nonlinear regression scheme to handle such challenge. In addition to simulated signals, we show its applicability to two physiological signals, impedance pneumography and photoplethysmogram, and examine how acutely the proposed decomposition scheme help extract physiological information. Comparisons with existing solutions, including linear regression, recursive diffeomorphism-based regression and multiresolution mode decomposition, supports the advantages for our proposal.

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Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform

Cited by 17 publications

References 51 publications

Soft and flexible piezoelectric smart patch for vascular graft monitoring based on Aluminum Nitride thin film

Soft and flexible piezoelectric smart patch for vascular graft monitoring based on Aluminum Nitride thin film

Development of a simulation model of a photoplethysmographic signal under psychoemotional stress

Decomposing non-stationary signals with time-varying wave-shape functions

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