[Pulse transit time detection based on waveform time domain feature and dynamic difference threshold].
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Photoplethysmogram
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Photoplethysmogram
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Photoplethysmography (PPG) is a low cost, non-invasive optical technology to detect the volumetric changes of blood circulation at the surface of skin. While the medical indication of components of PPG signals in the form of pulse wave are not yet fully understood, it is vastly agreed that they carry valuable pathophysiological information related to the cardiovascular system. Going beyond just dealing with frequency and time domain features of the pulse wave, as well as the first and second derivatives of the wave commonly seen in many of the relevant work, we applied a K-MEANS improved algorithm for feature extraction based on selected time domain parameters: K1 (systolic area), K2 (diastolic area) and K (entire pulse wave area). The extracted characteristic waveforms under the same light intensity could achieve average confidence level of 90% or higher. The stationary wavelet transform was adopted to further analyze the characteristic waveform by calculating the wavelet entropy; We then trained a Probability Neural Network (PNN) model using the wavelet entropy and other time domain characteristic parameters. It is found that the trained PNN model performs well in analyzing characteristic waveform to distinguish between health condition and severe arterial stenosis.
Photoplethysmogram
Pulse Wave Analysis
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Beat (acoustics)
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Photoplethysmogram
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Derivative (finance)
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Photoplethysmography (PPG) is an optical technique for detection of blood volume changes in the microvascular bed of a biological tissue. Many aspects of the PPG signal formation are still unclear. In particular, it is not known how the shape of a registered PPG signal depends on the geometry of tissue illumination. The aim of this study is to model the PPG waveform using the Monte Carlo (MC) method. For this, we developed a three-layer optical model of the skin in a reflectance geometry and verified it experimentally for different wavelengths (660, 810, and 940 nm) and source-detector distances (from 4 to 10 mm). The MC simulation results showed that the PPG waveform depends on the source-detector distance. The most pronounced diastolic wave is observed at the distance of 6 mm for the wavelength of 810 nm. The results obtained can be used for the development of reflectance PPG sensors.
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Because Photoplethysmography(PPG) signal is disturbed by breath,position and movement,it is necessary to detect and remove disturbed signal before computing the user's vital sign.Based on Euclidean distance,pulse wave transit time and coefficient of the pulse waveform,this paper presents a parameter,called associate factor of pulse wave,which is used to detect abnormal pulse waveform.The PPG time serious is divided into pieces using R wave of Electrocardiogram(ECG) signal;the time line of each piece of PPG signal is normalized in order to inhibit sensitive issue of time line in Euclidean distance calculation.The accuracy and feasibility of the proposed algorithm in identifying abnormal waveform are verified by analyzing measured pulse wave signal.
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Photoplethysmogram
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Photoplethysmogram
Pulsatile flow
Arrival time
Pulse Wave Analysis
Arterial tree
Fiducial marker
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Based on the working principle of pulse image sensor,the finite element model of pulse image sensor is established by the software Abaqus.The Abaqus/Explicit is used to make the dynamic analysis of the model.By application of Abaqus/Explicit dynamic analysis of the model,the pulse waveform of the different points on the film is extracted.Through the analysis of the displacements of the different points.Based on the analysis of the pulse signal in time domain,the characteristic parameters of the pulse signal in time domain are extracted.On the basis of this research,further research on the thin film 3D dynamic changes can be made.
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The arterial pulse waveform dependence on applied pressure was experimentally analyzed. An experimental device was built and tests were conducted. Pressure was applied to the brachial artery and the analysed photoplethysmographic waveform was registered from radial artery. The photoplethysmographic signal was analyzed by calculating the differences between pulse waveforms. In addition waveform amplitude and the slope of the rising front were studied. When the applied pressure was lowered to the level where the piezoelectric signal amplitude is near its maximum, the differences between pulse waveforms decrease. It was concluded that there is a critical pressure level and below this the waveform is no longer influenced.
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Pulse pressure
Photoplethysmogram
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The pulse wave at the human radial artery is closely related to the health status of the cardiovascular system. In this paper, the morphological features of the pulse wave were used to establish a diagnostic model for the severity of coronary atherosclerotic heart disease (CAD). Features of waveform variations were extracted from pulse wave sequences by building a deep learning network, Temporal Convolutional Network (TCN), which mined more detailed waveform information and obtained more comprehensive features of waveform morphology than the classical time domain features extraction method, thus established a TCN-based CAD severity diagnostic model (TCSDM) with better performance. The 64 features extracted by TCN have shown significant differences between the three classes of CAD samples at the 0.05 level, which have provided additional basis for the model's classification decisions. The accuracy of TCSDM has reached 91.17%, an 11.93% improvement compared to the Random Forest-based diagnostic model using classical time domain features. The proposed method for the acquisition of pulse wave morphological features can effectively extract the differential features of different pulse waves. And this method has a great application value in the remote diagnosis of CAD severity because it's non-invasive, rapid and low-cost.
Pulse Wave Analysis
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Pulse parameters calculated from the LDF waveform based on time-domain synchronized averaging analysis were shown to be able to discriminate the difference in microvascular resistance, however its applicability depends seriously on the assumption of signal stationarity. In this study, our aim is to investigate the effect of pulse number, which may destroy the signal stationarity, on the pulse LDF parameters. The study presented here has established the criteria for pulse number to achieve the signal stationarity so that the microcirculatory discriminability of the pulse-based time-averaging analysis on LDF signal can be improved. The proposed quantitative method to verify the assumption of signal stationarity when utilizing time-averaging can also be applied to analysis of other bio-signals.
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Signal averaging
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