Difference in pulse transit time between populations: a comparison between Caucasian and Chinese children in Australia

Foo, Jong Yong Abdiel; Lim, Chu Sing

doi:10.1080/03091900600632694

Cited by 7 publications

(3 citation statements)

References 16 publications

(26 reference statements)

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“…Whereas, in the majority of the studies this parameter was suggested as a blood pressure surrogate, PTT has gradually become a popular index of arterial stiffness (Kingwell et al 1997;Nichols 2005). Most importantly, pulse transit time has been used as a measure of vascular health and condition across life span (Safar et al 2003;Vaitkevicius et al 1993;Zahedi et al 2007) in different populations (Foo and Lim 2008).…”

Section: Introductionmentioning

confidence: 99%

The Role of Pulse Transit Time as an Index of Arterial Stiffness During Exercise

Kounalakis

Geladas

2009

Cardiovasc Eng

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The aim of the present study was to investigate, whether pulse transit time (PTT), a popular index of arterial stiffness at rest, can be also used as such, during steady state exercise. For this purpose, twelve male volunteers exercised on a cycle ergometer for 70 min on three separate occasions whereas, cycling cadence and workload were manipulated in order to produce diverse cardiorespiratory responses. PTT, blood pressure, cardiac output and respiratory frequency were measured during exercise. Resistance to systole and total peripheral resistance were calculated by the ratio of systolic pressure, and mean arterial pressure over cardiac output, respectively. All subjects across all conditions, showed a negative linear correlation (P < 0.01) between changes in PTT and systolic pressure (SP) (r = -0.66), changes in cardiac output (r = -0.76), and respiratory frequency (r = -0.40), whereas PTT was positively correlated (P < 0.05) with total peripheral resistance (r = 0.31), the SP to cardiac output ratio (r = 0.30) and plasma volume changes (r = 0.29). However, forward stepwise multiple regression analysis revealed that 71% (P < 0.001) of PTT changes from rest (DeltaPTT) variability was attributed to changes in cardiac output, SP and SP to cardiac output ratio. In the same model, total peripheral resistance did not exert significant influence on DeltaPTT variability. In conclusion, PTT is a reflection not only of SP but also of cardiac output changes per se and in combination with cardiac output (SP to cardiac output ratio) and should not be used as a pure marker of arterial stiffness under marked exercise cardiovascular and respiratory perturbations.

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Section: Introductionmentioning

confidence: 99%

The Role of Pulse Transit Time as an Index of Arterial Stiffness During Exercise

Kounalakis

Geladas

2009

Cardiovasc Eng

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“…Moreover, until now, AIx and PWV measured at different sites have not been evaluated in Chinese patients with type 2 diabetes and well‐matched controls. Because the differences in arterial stiffness among different ethnic groups have been previously reported,19,20 it is necessary to investigate the associations between diabetes and indices of arterial stiffness in Chinese inhabitants.…”

Section: Introductionmentioning

confidence: 99%

Type 2 Diabetes Is Associated With Increased Pulse Wave Velocity Measured at Different Sites of the Arterial System but Not Augmentation Index in a Chinese Population

Zhang

Yang

et al. 2011

Clin Cardiol

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Background: Patients with type 2 diabetes have increased stiffness of central elastic arteries. However, whether peripheral muscular artery stiffness is equally affected by the disease remains sparsely examined. Moreover, the association between pulse wave velocity (PWV) and augmentation index (AIx) in diabetes is poorly understood. Hypothesis: Type 2 diabetes is associated with the alterations in arterial stiffness (PWV and AIx) in a community‐based population. Methods: A total of 79 Chinese patients with type 2 diabetes and 79 sex‐, age‐ (±3 years), and body mass index‐ (±2 kg/m2) matched healthy controls were studied. Carotid‐femoral pulse wave velocity (CF‐PWV), carotid‐radial pulse wave velocity (CR‐PWV), and carotid‐ankle pulse wave velocity (CA‐PWV) were calculated from tonometry waveforms and body surface measurements, whereas AIx was assessed using pulse wave analyses. Results: In univariate analysis, patients with type 2 diabetes showed increased CF‐PWV (P < 0.001), CR‐PWV (P = 0.012), and CA‐PWV (P = 0.016), and lower AIx (P = 0.017) than the control group. In multiple linear regression models adjusting for covariates, type 2 diabetes remained a significant determinant of CF‐PWV. Fasting glucose was associated with CR‐PWV but was not related to CA‐PWV or AIx. Conclusions: Our findings suggest that patients with type 2 diabetes have increased central and peripheral artery stiffness, but preserved AIx compared to controls. Diabetes was a predictor of central artery stiffness, and glucose was a determinant of peripheral artery stiffness. © 2011 Wiley Periodicals, Inc. This work was supported by a grant from the National Nature Science Foundation of China (30872713), Beijing Natural Science Foundation (7082083), and is a key project of the Capital Development Foundation (2009–2038) of Dr. Ping Ye. The authors have no other funding, financial relationships, or conflicts of interest to disclose. Dr. Zhang and Dr. Bai contributed equally to this work.

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“…Van Velzen, et al identified 43 different methods to determine PTT [20] between an ECG R-peak and a PPG signal [21][22][23][24][25]. A PTT for ECG-PPG measurements is called Pulse Arrival Time (PAT).…”

Section: Introductionmentioning

confidence: 99%

Machine Learned Pulse Transit Time (MLPTT) Measurements from Photoplethysmography

Mehrgardt

Khushi

Withana

et al. 2020

Neural Information Processing

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Pulse transit time (PTT) provides a cuffless method to measure and predict blood pressure, which is essential in long term cardiac activity monitoring. Photoplethysmography (PPG) sensors provide a low-cost and wearable approach to obtain PTT measurements. The current approach to calculating PTT relies on quasi-periodic pulse event extractions based on PPG local signal characteristics. However, due to inherent noise in PPG, especially at uncontrolled settings, this approach leads to significant errors and even missing potential pulse events. In this paper, we propose a novel approach where global features (all samples) of the time-series data are used to develop a machine learning model to extract local pulse events. Specifically, we contribute 1) a new noise resilient machine learning model to extract events from PPG and 2) results from a study showing accuracy over state of the art (e.g. HeartPy) and 3) we show that MLPTT outperforms HeartPy peak detection, especially for noisy photoplethysmography data.

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Difference in pulse transit time between populations: a comparison between Caucasian and Chinese children in Australia

Cited by 7 publications

References 16 publications

The Role of Pulse Transit Time as an Index of Arterial Stiffness During Exercise

The Role of Pulse Transit Time as an Index of Arterial Stiffness During Exercise

Type 2 Diabetes Is Associated With Increased Pulse Wave Velocity Measured at Different Sites of the Arterial System but Not Augmentation Index in a Chinese Population

Machine Learned Pulse Transit Time (MLPTT) Measurements from Photoplethysmography

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