Comparing blood pressure measurements between a photoplethysmography-based and a standard cuff-based manometry device

Nachman, Dean; Gepner, Yftach; Goldstein, Nir; Kabakov, Eli; Ishay, Arik Ben; Littman, Romi; Azmon, Yuval; Jaffe, Eli; Eisenkraft, Arik

doi:10.1038/s41598-020-73172-3

Cited by 73 publications

(58 citation statements)

References 25 publications

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“…Thus far, studies investigating maternal hemodynamics during labor and delivery have yielded conflicting results, especially regarding magnitude and timing of hemodynamic adaptations. Previous studies suggested inconsistent results on the effect of EA on maternal hemodynamics during labor [19,[23][24][25][26]. EA induces a sympathetic blockade that causes vasodilatation and a decrease in venous return to the heart, which can result in maternal hypotension.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, abrupt onset of pain relief may trigger a reduction in blood pressure. However, these effects are seen in only some of the studies and reported clinically in only 0-14% of laboring women receiving EA [23][24][25][26][27]. Recently, Ashwal et al [19] compared maternal hemodynamics following vaginal and cesarean deliveries determined by a whole body bioimpedance-based device.…”

Section: Discussionmentioning

confidence: 99%

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Continuous Maternal Hemodynamics Monitoring at Delivery Using a Novel, Noninvasive, Wireless, PPG-Based Sensor

Atzmon

Ishay²,

Hallak

et al. 2020

JCM

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Objective: To evaluate continuous monitoring of maternal hemodynamics during labor and delivery utilizing an innovative, noninvasive, reflective photoplethysmography-based device. Study design: The Biobeat Monitoring Platform includes a wearable wristwatch monitor that automatically samples cardiac output (CO), blood pressure (BP), stroke volume (SV), systemic vascular resistance (SVR), heart rate (HR) every 5 s and uploads all data to a smartphone-based app and to a data cloud, enabling remote patient monitoring and analysis of data. Low-risk parturients at term, carrying singletons pregnancies, were recruited at early delivery prior to the active phase. Big data analysis of the collected data was performed using the Power BI analysis tool (Microsoft). Next, data were normalized to visual presentation using Excel Data Analysis and the regression tool. Average measurements were compared before and after rupture of membranes, epidural anesthesia, fetal delivery, and placental expulsion. Results: Eighty-one parturients entered analysis. Epidural anesthesia was associated with a slight elevation in CO (5.5 vs. 5.6, L/min, 10 min before and after EA, p < 0.05) attributed to a non-significant increase in both HR and SV. BP remained stable as of counter decrease in SVR (1361 vs. 1319 mmHg⋅min⋅mL−1, 10 min before and after EA, p < 0.05). Fetal delivery was associated with a peak in CO after which it rapidly declined (6.0 vs. 7.2 vs. 6.1 L/min, 30 min before vs. point of delivery vs. after delivery, p < 0.05). The mean BP remained stable throughout delivery with a slight increase at fetal delivery (92 vs. 95 vs. 92.1 mmHg, p < 0.05), reflecting the increase in CO and decrease in SVR (1284 vs. 1112 vs. 1280 mmHg⋅min⋅mL−1, p < 0.05)with delivery. Placental expulsion was associated with a second peak in CO and decrease in SVR. Conclusions: We presented a novel application of noninvasive hemodynamic maternal monitoring throughout labor and delivery for both research and clinical use.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Continuous Maternal Hemodynamics Monitoring at Delivery Using a Novel, Noninvasive, Wireless, PPG-Based Sensor

Atzmon

Ishay²,

Hallak

et al. 2020

JCM

Self Cite

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“…A time-consuming approach was suggested in [52] for estimating SBP and DBP based on back-propagation error neural networks. Several techniques were implemented to reduce feature redundancy, optimize the BP estimation, and determine the initial weights and threshold of the network.…”

Section: Background and Related Workmentioning

confidence: 99%

A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool

Fati

Muneer

Akbar³

et al. 2021

Symmetry

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High blood pressure (BP) may lead to further health complications if not monitored and controlled, especially for critically ill patients. Particularly, there are two types of blood pressure monitoring, invasive measurement, whereby a central line is inserted into the patient’s body, which is associated with infection risks. The second measurement is cuff-based that monitors BP by detecting the blood volume change at the skin surface using a pulse oximeter or wearable devices such as a smartwatch. This paper aims to estimate the blood pressure using machine learning from photoplethysmogram (PPG) signals, which is obtained from cuff-based monitoring. To avoid the issues associated with machine learning such as improperly choosing the classifiers and/or not selecting the best features, this paper utilized the tree-based pipeline optimization tool (TPOT) to automate the machine learning pipeline to select the best regression models for estimating both systolic BP (SBP) and diastolic BP (DBP) separately. As a pre-processing stage, notch filter, band-pass filter, and zero phase filtering were applied by TPOT to eliminate any potential noise inherent in the signal. Then, the automated feature selection was performed to select the best features to estimate the BP, including SBP and DBP features, which are extracted using random forest (RF) and k-nearest neighbors (KNN), respectively. To train and test the model, the PhysioNet global dataset was used, which contains 32.061 million samples for 1000 subjects. Finally, the proposed approach was evaluated and validated using the mean absolute error (MAE). The results obtained were 6.52 mmHg for SBS and 4.19 mmHg for DBP, which show the superiority of the proposed model over the related works.

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“…To the best of our knowledge, this is the only cleared wearable wireless medical-grade device to provide all these measurements. The sensor tracks vital signs derived from changes in the pulse contour, following baseline calibration using an approved non-invasive, cuffbased device, and is based on Pulse Wave Transit Time (PWTT) technology, combined with Pulse Wave Analysis (PWA) (see, for example, 15,16 ). The sensor continuously collected data at 10minute intervals for the entire duration of the 96-hour experiment.…”

Section: The Chest-patch Sensormentioning

confidence: 99%

Short-term effects of BNT162b2 mRNA COVID-19 vaccination on physiological measures: a prospective study

Gepner

Mofaz

Oved

et al. 2021

Preprint

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Background: Clinical trial guidelines for assessing the safety of vaccines, including the FDA criteria, are primarily based on subjective, self-reported questionnaires. Despite the tremendous technological advances in recent years, objective, continuous assessment of physiological measures post-vaccination is rarely performed. Methods: To evaluate the short-term effects of the BNT162b2 COVID-19 vaccine on physiological measures, we conducted a prospective observational study during the mass vaccination campaign in Israel. 160 individuals >18 years who were not previously found to be COVID-19 positive and who received the second dose of the COVID-19 vaccine between 1 January 2021, and 13 March 2021 were equipped with a chest-patch sensor and a dedicated mobile application. The chest-patch sensor continuously measured 13 physiological vitals one day before the inoculation (baseline), for four days: heart rate, blood oxygen saturation, respiratory rate, systolic and diastolic blood pressure, pulse pressure, mean arterial pressure, heart rate variability, stroke volume, cardiac output, cardiac index, systemic vascular resistance, and body temperature. The mobile application collected daily self-reported questionnaires starting one day before the inoculation, for 15 days on local and systemic reactions, sleep quality, stress levels, physical activity, and mood levels. Findings: Within the first 48 hours post-vaccination, we identified significant changes (p-value <0.05) in nearly all 13 chest-patch indicators compared to their baseline levels. 48.5% (n=78) reported no local or systemic reaction. Nevertheless, we identified considerable changes in chest-patch indicators during the first 48 hours post-vaccination also in this group of presumably asymptomatic participants. Within three days from vaccination, these measures returned to baseline levels in both groups, further supporting the safety of the vaccine. Interpretation: Our work underscores the importance of obtaining objective physiological data in addition to self-reported questionnaires when performing clinical trials, particularly in ones conducted in very short time frames. Funding: The European Research Council (ERC) project #949850.

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Comparing blood pressure measurements between a photoplethysmography-based and a standard cuff-based manometry device

Cited by 73 publications

References 25 publications

Continuous Maternal Hemodynamics Monitoring at Delivery Using a Novel, Noninvasive, Wireless, PPG-Based Sensor

Continuous Maternal Hemodynamics Monitoring at Delivery Using a Novel, Noninvasive, Wireless, PPG-Based Sensor

A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool

Short-term effects of BNT162b2 mRNA COVID-19 vaccination on physiological measures: a prospective study

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