The Establishment of a Non-Invasive Continuous Blood Pressure Measure System Based on Pulse Transit Time

2008 ◽  
Author(s):  
Dingli Li ◽  
Yi Pan ◽  
Hang Chen ◽  
Shuming Ye ◽  
Hong Yan
Keyword(s):  
Blood Pressure ◽  
Transit Time ◽  
Pulse Transit Time ◽  
Non Invasive ◽  
Blood Pressure Measure ◽  
Pressure Measure ◽  
Continuous Blood Pressure

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

scholarly journals Analyzing 24-Hour Blood Pressure Measurements with a Novel Cuffless Pulse Transit Time Device in Clinical Practice—Does the Software for Heartbeat Detection Matter?

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 361
Author(s):  
Leo Kilian ◽  
Philipp Krisai ◽  
Thenral Socrates ◽  
Christian Arranto ◽  
Otmar Pfister ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Transit Time ◽  
Pulse Transit Time ◽  
Population Level ◽  
Raw Data ◽  
Invasive Blood Pressure ◽  
Individual Level ◽  
Non Invasive ◽  
The Individual ◽  
The Impact

Background: The Somnotouch-Non-Invasive-Blood-Pressure (NIBP) device delivers raw data consisting of electrocardiography and photoplethysmography for estimating blood pressure (BP) over 24 h using pulse-transit-time. The study’s aim was to analyze the impact on 24-hour BP results when processing raw data by two different software solutions delivered with the device. Methods: We used data from 234 participants. The Somnotouch-NIBP measurements were analyzed using the Domino-light and Schiller software and compared. BP values differing >5 mmHg were regarded as relevant and explored for their impact on BP classification (normotension vs. hypertension). Results: Mean (±standard deviation) absolute systolic/diastolic differences for 24-hour mean BP were 1.5 (±1.7)/1.1 (±1.3) mm Hg. Besides awake systolic BP (p = 0.022), there were no statistically significant differences in systolic/diastolic 24-hour mean, awake, and asleep BP. Twenty four-hour mean BP agreement (number (%)) between the software solutions within 5, 10, and 15 mmHg were 222 (94.8%), 231 (98.7%), 234 (100%) for systolic and 228 (97.4%), 232 (99.1%), 233 (99.5%) for diastolic measurements, respectively. A BP difference of >5 mmHg was present in 24 (10.3%) participants leading to discordant classification in 4–17%. Conclusion: By comparing the two software solutions, differences in BP are negligible at the population level. However, at the individual level there are, in a minority of cases, differences that lead to different BP classifications, which can influence the therapeutic decision.

scholarly journals Novel computation of pulse transit time from multi-channel PPG signals by wavelet transform

2016 ◽  
Vol 2 (1) ◽  
pp. 209-213 ◽  
Author(s):  
Alexandru-Gabriel Pielmuş ◽  
Maik Pflugradt ◽  
Timo Tigges ◽  
Michael Klum ◽  
Aarne Feldheiser ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Wavelet Transform ◽  
Transit Time ◽  
Sensor Node ◽  
Pulse Transit Time ◽  
Time Data ◽  
Dual Mode ◽  
Non Invasive ◽  
System Methodology ◽  
Biomedical Community

AbstractBeing able to accurately monitor blood pressure in a reliable, truly non-invasive manner is a highly sought after goal within the biomedical community. In this paper we propose and assess a system, methodology and algorithm for unobtrusively obtaining true pulse transit time data from readily accessible peripheral locations, such as the hand, using a highly synchronous body-sensor-network encompassing an electrocardiogram- and dual mode photoplethysmogram sensor node. The results suggest the feasibility of acquiring such data, which strongly correlates with the recorded reference blood pressure, and can therefore be further employed to track changes thereof.

scholarly journals Pengukuran Tekanan Darah Non-Invasive Tanpa Manset Menggunakan Metode Pulse Transit Time Berbasis Machine Learning Multivariat Regresi

2019 ◽  
Vol 10 (1) ◽  
pp. 61
Author(s):  
Ernia Susana
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Transit Time ◽  
Pressure Measurement ◽  
Blood Pressure Measurement ◽  
Sequential Estimation ◽  
Pulse Transit Time ◽  
P Value ◽  
Oscillometric Method ◽  
Non Invasive

<p>Currently used non-invasive blood pressure (NIBP) measurements (oscillometric method) has disadvantages related to pumping cuffs which can cause discomfort for patients due to pressure from pumping cuffs.  The aim of this study was to measure blood pressure in a non-invasive manner without cuffs with the Pulse Transit Time (PTT) methodbase on machine learning technology.The blood pressure measurement by the PTT method is obtained from the calculation of the distance of the R-ECG wave with the peak signal <em>photoplethysmogram</em><em> </em>(PPG). The main problem of the PTT method in some previous studies is that the estimation of systolic (SBP) and diastolic (DBP) values is still inaccurate. The blood pressure measurement method in this study used a combination of PTT calculations with machine learning multivariate regression. Therefore expected to obtain a more accurate estimate of systolic (SBP) and diastolic blood pressure (DBP). This study is a laboratory experiment research on 30 healthy volunteers aged 20 ± 1 years. The measurement of the blood pressure value of the PTT-to-oscillometric method is 5 ± 5 mmHg. The blood pressure values generated by this PTT method have a p-value for the sequential estimation of SBP and DBP of 0.7374 and 0.0262.<strong></strong></p>

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