Blood pressure measurement based on pulse transit time from different positioned photoplethysmography signals

Kao, Yu Cheng; Wang, Chun-Hsiung; Lee, Shu-Sheng; Wu, Wen-Jong; Lee, Chih‐Kung

doi:10.1117/12.2555337

Cited by 3 publications

(4 citation statements)

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“…Other examples are using two PPG measurements to define the PTT, e.g. between the earlobe and finger (Block et al 2020, Kao et al 2020, or utilizing a high-speed camera to capture PPG signals from the face and palm of the hand (Jeong and Finkelstein 2016).…”

Section: Effect Of Sensor Placement On Pttmentioning

confidence: 99%

Blood pressure wave propagation—a multisensor setup for cerebral autoregulation studies

et al. 2021

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Objective. Cerebral autoregulation is critically important to maintain proper brain perfusion and supply the brain with oxygenated blood. Non-invasive measures of blood pressure (BP) are critical in assessing cerebral autoregulation. Wave propagation velocity may be a useful technique to estimate BP but the effect of the location of the sensors on the readings has not been thoroughly examined. In this paper, we were interested in studying whether the propagation velocity of a pressure wave in the direction from the heart to the brain may differ compared with propagation from the heart to the periphery, as well as across different physiological tasks and/or health conditions. Using non-invasive sensors simultaneously placed at different locations of the human body allows for the study of how the propagation velocity of the pressure wave, based on pulse transit time (PTT), varies across different directions. Approach. We present a multi-sensor BP wave propagation measurement setup intended for cerebral autoregulation studies. The presented sensor setup consists of three sensors, one placed on each of the neck, chest and finger, allowing simultaneous measurement of changes in BP propagation velocity towards the brain and to the periphery. We show how commonly tested physiological tasks affect the relative changes of PTT and correlations with BP. Main results. We observed that during maximal blow, valsalva and breath hold breathing tasks, the relative changes of PTT were higher when PTT was measured in the direction from the heart to the brain than from the heart to the peripherals. In contrast, during a deep breathing task, the relative change in PTT from the heart to the brain was lower. In addition, we present a short literature review of the PTT methods used in brain research. Significance. These preliminary data suggest that the physiological task and direction of PTT measurement may affect relative PTT changes. The presented three-sensor setup provides an easy and neuroimaging compatible method for cerebral autoregulation studies by allowing measurement of BP wave propagation velocity towards the brain versus towards the periphery.

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Section: Effect Of Sensor Placement On Pttmentioning

confidence: 99%

Blood pressure wave propagation—a multisensor setup for cerebral autoregulation studies

et al. 2021

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“…iPPG can also be applied to measure blood oxygen concentration [7]. Furthermore, extracting iPPG signals from the face or palm can be used to assess blood pressure [8]. However, this method still requires parameters from blood pressure models, often obtained by combining it with ECG data.…”

Section: Introductionmentioning

confidence: 99%

Integrating recurrent neural network (RNN) and Navier-Stokes equations for noncontact blood pressure assessment

Luo,

Chiang,

Lin

et al. 2024

Health Monitoring of Structural and Biological Systems XVIII

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The study is to establish the non-contact blood pressure measurement model. We propose a novel hybrid blood pressure assessment model. This model employs digital signal processing (DSP) to process the Imaging Photoplethysmography (iPPG) signal, utilizing Support Vector Machine (SVM) classification to determine the optimal signal location through three parameters. It is then compared with a PPG device. Through a CNN-LSTM model, it aims to reconstruct the ideal iPPG signal, transforming signals from the dermal layer into radial artery signals. Based on the Beer-Lambert law, the natural logarithm of iPPG intensity is proportional to blood flow velocity. Thus, a regression model for mean arterial pressure is developed in this work using heart rate and the intensity of iPPG signals. In conclusion, statistical test results confirm the validity of this study, indicating significant potential for the future development of noncontact blood pressure monitoring.

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“…Some research has shown how to construct a mathematical blood flow model, which is derived from Navier-Stokes equations under the assumption that the blood vessel is a cylindrical pipe, and that blood is an incompressible Newtonian fluid. Based on a blood flow model, and adopting a Bramwell-Hill equation and a Moens-Korteweg equation, our group simplified the BP model as a regression model from a previous study [4]. Furthermore, we also adopted a regression model which is composed of a pulse transit time (PTT), HR, blood flow velocity, and R-square value or p-value to predict blood pressure with good results [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…Based on a blood flow model, and adopting a Bramwell-Hill equation and a Moens-Korteweg equation, our group simplified the BP model as a regression model from a previous study [4]. Furthermore, we also adopted a regression model which is composed of a pulse transit time (PTT), HR, blood flow velocity, and R-square value or p-value to predict blood pressure with good results [4,5]. Although our blood pressure measurement system can measure blood pressure with good accuracy, the influence by physiology has not yet been considered.…”

Section: Introductionmentioning

confidence: 99%

Continuous blood pressure monitoring from an autonomic nervous system perspective

Chiang

Tai²,

Liu³

et al. 2023

Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables IV

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We propose a novel continuous blood pressure monitoring system which is based on an autonomic nervous system and which considers blood volume simultaneously since both affect blood pressure. An autonomic nervous system regulates blood pressure while blood volume is known to be proportional to the photoplethysmography (PPG) signal. To overcome the limitation of taking blood pressure using a conventional cuff inflating instrument, we designed a system which can achieve continuous blood pressure monitoring. In this research, we used a set of near-infrared light source (940nm) to create a divergent light which was collimated as a uniform beam incident to a wrist surface through a Fourier optics designed transfer lens. We found that the signals became more stable due to the uniform illumination and could be received by a detector. From the signals, we found that the blood volume when converted from blood velocity as measured by an ultrasound probe, showed a strong correlation with the signals. The heart rate variability analyzed from the signals, including time-domain (HR and SDNN) and frequency-domain (LF and HF) indices, could be viewed as physical models since these indices reflect the functions of an autonomic nervous system. Moreover, the research derived regression models can estimate blood pressure. Although it is not common to assess blood pressure from the perspective of an autonomic nervous system and blood flow simultaneously, our research approach seems logical. Our results show the potential for this novel system to be used for blood pressure health monitoring.

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Blood pressure measurement based on pulse transit time from different positioned photoplethysmography signals

Cited by 3 publications

References 0 publications

Blood pressure wave propagation—a multisensor setup for cerebral autoregulation studies

Blood pressure wave propagation—a multisensor setup for cerebral autoregulation studies

Integrating recurrent neural network (RNN) and Navier-Stokes equations for noncontact blood pressure assessment

Continuous blood pressure monitoring from an autonomic nervous system perspective

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