A rat-friendly modification of the non-invasive tail-cuff to record blood pressure

Lab Animal ◽  
2017 ◽  
Vol 46 (6) ◽  
pp. 251-253 ◽  
Author(s):  
Boris Lipták ◽  
Barbara Kaprinay ◽  
Zdenka Gáspárová
Keyword(s):  
Blood Pressure ◽  
Non Invasive ◽  
Record Blood Pressure ◽  
Tail Cuff

History of vascular reactivity models and their involvement in hypertension pathogenesis

VASA ◽  
2017 ◽  
Vol 46 (6) ◽  
pp. 431-439 ◽  
Author(s):  
Ana Gabriela Conceição-Vertamatti ◽  
Filipy Borghi ◽  
Fernando Canova ◽  
Dora Maria Grassi-Kassisse
Keyword(s):  
Blood Pressure ◽  
Adrenergic Receptors ◽  
Vascular Reactivity ◽  
Multifactorial Disease ◽  
Pharmacological Treatments ◽  
Beta Adrenergic Receptors ◽  
Vascular Area ◽  
History Of ◽  
Record Blood Pressure ◽  
Advance Knowledge

Abstract. Hypertension is a silent and multifactorial disease. Over two centuries ago, the first device to record blood pressure was developed, making it possible to determine normotension and to establish criteria for hypertension. Since then, several studies have contributed to advance knowledge in this area, promoting significant advances in pharmacological treatments and, as a result, increasing survival of hypertensive people. The main models developed for the study of hypertension and the main findings in the vascular area are included in this review. We considered aspects related to vascular reactivity, changes in the population, and action of beta adrenergic receptors in the pathogenesis of hypertension.

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

scholarly journals Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Sen Yang ◽  
Yaping Zhang ◽  
Siu-Yeung Cho ◽  
Ricardo Correia ◽  
Stephen P. Morgan
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Deep Learning ◽  
Absolute Error ◽  
Learning Model ◽  
Physiological Measurement ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Blood Pressure Estimation ◽  
Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

scholarly journals Association between central non-dipping pattern and platelet morphology in adults with type 1 diabetes without cardiovascular disease: a cross-sectional study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michal Kulecki ◽  
Dariusz Naskret ◽  
Mikolaj Kaminski ◽  
Dominika Kasprzak ◽  
Pawel Lachowski ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Type 1 Diabetes ◽  
Smoking Status ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Distribution Width ◽  
Non Invasive ◽  
Platelet Morphology ◽  
Oscillometric Device

AbstractThe non-dipping pattern is nighttime systolic blood pressure (SBP) fall of less than 10%. Several studies showed that the non-dipping pattern, increased mean platelet volume (MPV), and platelet distribution width (PDW) are associated with elevated cardiovascular risk. Hypertensives with the non-dipping pattern have higher MPV than the dippers but this relationship was never investigated among people with type 1 diabetes mellitus (T1DM). This study aimed to investigate the association between the central dipping pattern and platelet morphology in T1DM subjects. We measured the central and brachial blood pressure with a validated non-invasive brachial oscillometric device—Arteriograph 24—during twenty-four-hour analysis in T1DM subjects without diagnosed hypertension. The group was divided based on the central dipping pattern for the dippers and the non-dippers. From a total of 62 subjects (32 males) aged 30.1 (25.7–37) years with T1DM duration 15.0 (9.0–20) years, 36 were non-dippers. The non-dipper group had significantly higher MPV (MPV (10.8 [10.3–11.5] vs 10.4 [10.0–10.7] fl; p = 0.041) and PDW (13.2 [11.7–14.9] vs 12.3 [11.7–12.8] fl; p = 0.029) than dipper group. Multivariable logistic regression revealed that MPV (OR 3.74; 95% CI 1.48–9.45; p = 0.005) and PDW (OR 1.91; 95% CI 1.22–3.00; p = 0.005) were positively associated with central non-dipping pattern adjusting for age, sex, smoking status, daily insulin intake, and height. MPV and PDW are positively associated with the central non-dipping pattern among people with T1DM.

scholarly journals An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1867
Author(s):  
Tasbiraha Athaya ◽  
Sunwoong Choi
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Health ◽  
Estimation Method ◽  
Absolute Error ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Arterial Blood ◽  
Blood Pressure Waveform ◽  
The Mean ◽  
Mimic Iii

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

scholarly journals Feasibility of non‐invasive measurement of central blood pressure and arterial stiffness in shock

2021 ◽  
Author(s):  
Konstantinos Markakis ◽  
Nikolaos Pagonas ◽  
Eleni Georgianou ◽  
Panagiota Zgoura ◽  
Benjamin J. Rohn ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Arterial Stiffness ◽  
Central Blood Pressure ◽  
Non Invasive ◽  
Invasive Measurement

scholarly journals Continuous non-invasive blood pressure during continuous repositioning by pulse transit time

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Hieyong Jeong ◽  
Kayo Yoshimoto ◽  
Tianyi Wang ◽  
Takafumi Ohno ◽  
Kenji Yamada ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Transit Time ◽  
Pulse Transit Time ◽  
Invasive Blood Pressure ◽  
Non Invasive
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