Evaluation of the correlation between blood pressure and pulse transit time

He, Xiaodong; Goubran, Rafik; Liu, X. P.

doi:10.1109/memea.2013.6549697

Cited by 20 publications

(3 citation statements)

References 12 publications

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“…Furthermore, the measuring event itself can cause stress or anxiety in the subject, which can in turn influence the measured BP values. This is commonly known as the white-coat syndrome [5].…”

Section: Introductionmentioning

confidence: 99%

Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network

Slapničar

Mlakar

Luštrek

2019

Sensors

256

202

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Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using just a photoplethysmogram (PPG) to estimate BP is a potential solution investigated in our study. We analyzed the MIMIC III database for high-quality PPG and arterial BP waveforms, resulting in over 700 h of signals after preprocessing, belonging to 510 subjects. We then used the PPG alongside its first and second derivative as inputs into a novel spectro-temporal deep neural network with residual connections. We have shown in a leave-one-subject-out experiment that the network is able to model the dependency between PPG and BP, achieving mean absolute errors of 9.43 for systolic and 6.88 for diastolic BP. Additionally we have shown that personalization of models is important and substantially improves the results, while deriving a good general predictive model is difficult. We have made crucial parts of our study, especially the list of used subjects and our neural network code, publicly available, in an effort to provide a solid baseline and simplify potential comparison between future studies on an explicit MIMIC III subset.

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Section: Introductionmentioning

confidence: 99%

Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network

Slapničar

Mlakar

Luštrek

2019

Sensors

256

202

View full text Add to dashboard Cite

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“…There are two typical methods to measure PTT. The first [14] is to calculate the delay between Electrocardiogram (ECG) and PPG, and the second method [15] is to measure the delay of different PPGs acquired from different parts of the body. To improve the accuracy of the evaluation of BP, a new feature vector extraction algorithm was proposed by Li et al in [16].…”

Section: Introductionmentioning

confidence: 99%

Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography

Liu

Zhang

et al. 2015

Sci. China Inf. Sci.

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This paper proposes a novel wavelet neural network algorithm for the continuous and noninvasive dynamic estimation of blood pressure (BP). Unlike prior algorithms, the proposed algorithm capitalizes on the correlation between photoplethysmography (PPG) and BP. Complete BP waveforms are reconstructed based on PPG signals to extract systolic blood pressure (SBP) and diastolic blood pressure (DBP). To improve the robustness, Daubechies wavelet is implemented as the hidden layer node function for the neural network. An optimized neural network structure is proposed to reduce the computational complexity. Further, this paper investigates an inhomogeneous resilient backpropagation (IRBP) algorithm to calculate the weight of hidden layer nodes. The IRBP improves the convergence speed and reconstruction accuracy. Multiparameter intelligent monitoring in Intensive Care (MIMIC) databases, which contain a variety of physiological parameters captured from patient monitors, are used to validate this algorithm. The standard deviation σ between reconstructed and actual BP signals is 4.4797 mmHg, which satisfies the American National Standards of the Association for the Advancement of Medical Instrumentation. The reconstructed BP waveform can be used to extract the SBP and DBP, whose standard deviations σ are 2.91 mmHg and 2.41 mmHg respectively.Keywords photoplethysmogram (PPG), blood pressure (BP), Daubechies wavelet, neural network, inhomogeneous resilient backpropagation (IRBP) CitationLi P, Liu M, Zhang X, et al. Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography.

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“…The time delay (measured from the onset of the R-peak of the electrocardiogram [ECG]) required for a PW to travel from the left ventricle to any (distal) anatomical location is defined as the PTT, which includes the pre-ejection period (PEP) and pulse arrival time (PAT) [ 18 , 19 ]. PTT has been found to have strong correlations with systemic BP fluctuations [ 20 ], and has previously been studied in clinical settings [ 21 ]. Furthermore, PTT, thus, may reflect variations in PEP and central blood volume and is useful for early detection of non-hypotensive progressive central hypovolemia [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

Intraoperative Beat-to-Beat Pulse Transit Time (PTT) Monitoring via Non-Invasive Piezoelectric/Piezocapacitive Peripheral Sensors Can Predict Changes in Invasively Acquired Blood Pressure in High-Risk Surgical Patients

Nordine

Pille

Kraemer

et al. 2023

Sensors

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Background: Non-invasive tracking of beat-to-beat pulse transit time (PTT) via piezoelectric/piezocapacitive sensors (PES/PCS) may expand perioperative hemodynamic monitoring. This study evaluated the ability for PTT via PES/PCS to correlate with systolic, diastolic, and mean invasive blood pressure (SBPIBP, DBPIBP, and MAPIBP, respectively) and to detect SBPIBP fluctuations. Methods: PES/PCS and IBP measurements were performed in 20 patients undergoing abdominal, urological, and cardiac surgery. A Pearson’s correlation analysis (r) between 1/PTT and IBP was performed. The predictive ability of 1/PTT with changes in SBPIBP was determined by area under the curve (reported as AUC, sensitivity, specificity). Results: Significant correlations between 1/PTT and SBPIBP were found for PES (r = 0.64) and PCS (r = 0.55) (p < 0.01), as well as MAPIBP/DBPIBP for PES (r = 0.6/0.55) and PCS (r = 0.5/0.45) (p < 0.05). A 7% decrease in 1/PTTPES predicted a 30% SBPIBP decrease (0.82, 0.76, 0.76), while a 5.6% increase predicted a 30% SBPIBP increase (0.75, 0.7, 0.68). A 6.6% decrease in 1/PTTPCS detected a 30% SBPIBP decrease (0.81, 0.72, 0.8), while a 4.8% 1/PTTPCS increase detected a 30% SBPIBP increase (0.73, 0.64, 0.68). Conclusions: Non-invasive beat-to-beat PTT via PES/PCS demonstrated significant correlations with IBP and detected significant changes in SBPIBP. Thus, PES/PCS as a novel sensor technology may augment intraoperative hemodynamic monitoring during major surgery.

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Evaluation of the correlation between blood pressure and pulse transit time

Cited by 20 publications

References 12 publications

Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network

Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network

Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography

Intraoperative Beat-to-Beat Pulse Transit Time (PTT) Monitoring via Non-Invasive Piezoelectric/Piezocapacitive Peripheral Sensors Can Predict Changes in Invasively Acquired Blood Pressure in High-Risk Surgical Patients

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