scholarly journals Estimation of resting blood pressure using facial thermal images by separating acute stress variations

2021 ◽  
Author(s):  
Yuki Iwashita ◽  
Kent Nagumo ◽  
Kosuke Oiwa ◽  
Akio Nozawa
Keyword(s):  
Blood Pressure ◽  
Acute Stress ◽  
Medical Condition ◽  
Blood Pressure Monitoring ◽  
Estimation Method ◽  
Short Term ◽  
Thermal Images ◽  
Resting Blood Pressure ◽  
Blood Pressure Estimation ◽  
Stress Variations

AbstractThe increasing number of people with hypertension worldwide has become a matter of grave concern. Blood pressure monitoring using a non-contact measurement technique is expected to detect and control this medical condition. Previous studies have estimated blood pressure variations following an acute stress response based on facial thermal images obtained from infrared thermography devices. However, a non-contact resting blood pressure estimation method is required because blood pressure is generally measured in the resting state without inducing acute stress. Day-long blood pressure variations include short-term variations due to acute stress and long-term variations in circadian rhythms. The aim of this study is to estimate resting blood pressure from facial thermal images by separating and excluding short-term variations related to acute stress. To achieve this, short-term blood pressure variations components related to acute stress on facial thermal images were separated using independent component analysis. Resting blood pressure was estimated with the extracted independent components excluding the short-term components using multiple regression analysis. The results show that the proposed approach can accurately estimate resting blood pressure from facial thermal images, with a 9.90 mmHg root mean square error. In addition, features related to resting blood pressure were represented in the nose, lip, and cheek regions.

scholarly journals Enhancement of blood pressure estimation method via machine learning

2021 ◽  
Vol 60 (6) ◽  
pp. 5779-5796
Author(s):  
Nashat Maher ◽  
G.A. Elsheikh ◽  
W.R. Anis ◽  
Tamer Emara
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Estimation Method ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

PPG-BASED AUTOMATED ESTIMATION OF BLOOD PRESSURE USING PATIENT-SPECIFIC NEURAL NETWORK MODELING

2020 ◽  
Vol 20 (06) ◽  
pp. 2050037
Author(s):  
ABHISHEK CHAKRABORTY ◽  
DEBOLEENA SADHUKHAN ◽  
SAURABH PAL ◽  
MADHUCHHANDA MITRA
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Estimation Method ◽  
Estimation Methods ◽  
Patient Specific ◽  
Neural Network Modeling ◽  
Correction Factors ◽  
Healthcare Applications ◽  
Average Accuracy ◽  
Blood Pressure Estimation

Recently, photoplethysmography (PPG)-based techniques have been extensively used for cuff-less, automated estimation of blood pressure because of their inexpensive and effortless acquisition technology compared to other conventional approaches. However, most of the reported PPG-based, generalized BP estimation methods often lack the desired accuracy due to pathophysiological diversity. Moreover, some methods rely on several correction factors, which are not globalized yet and require further investigation. In this paper, a simple and automated systolic (SBP) and diastolic (DBP) blood pressure estimation method is proposed based on patient-specific neural network (NN) modeling. Initially, 15 time-plane PPG features are extracted and after feature selection, only four selected features are used in the NN model for beat-to-beat estimation of SBP and DBP, respectively. The proposed technique also presents reasonable accuracy while used for generalized estimation of BP. Performance of the algorithm is evaluated on 670 records of 50 intensive care unit (ICU) patients taken from MIMIC, MIMIC II and MIMIC Challenge databases. The proposed algorithm exhibits high average accuracy with (mean[Formula: see text][Formula: see text][Formula: see text]SD) of the estimated SBP as ([Formula: see text]) mmHg and DBP as ([Formula: see text]) mmHg. Compared to the other generalized models, the use of patient-specific approach eliminates the necessity of individual correction factors, thus increasing the robustness, accuracy and potential of the method to be implemented in personal healthcare applications.

SHORT-TERM REPRODUCIBILITY OF AMBULATORY BLOOD PRESSURE MONITORING

2018 ◽  
Vol 36 (Supplement 1) ◽  
pp. e175-e176
Author(s):  
K.P. Lee ◽  
B. Yip ◽  
S. Wong ◽  
K. Tsoi ◽  
J. Kwong ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Ambulatory Blood Pressure Monitoring ◽  
Ambulatory Blood Pressure ◽  
Blood Pressure Monitoring ◽  
Pressure Monitoring ◽  
Short Term

scholarly journals Increase of Short-Term Heart Rate Variability Induced by Blood Pressure Measurements during Ambulatory Blood Pressure Monitoring

2017 ◽  
Vol 2017 ◽  
pp. 1-5 ◽  
Author(s):  
Attila Frigy ◽  
Annamária Magdás ◽  
Victor-Dan Moga ◽  
Ioana Georgiana Coteț ◽  
Miklós Kozlovszky ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Ambulatory Blood Pressure ◽  
Pressure Measurement ◽  
Blood Pressure Monitoring ◽  
Blood Pressure Measurement ◽  
Pressure Measurements ◽  
Pressure Monitoring ◽  
Short Term

Objective.The possible effect of blood pressure measurements per se on heart rate variability (HRV) was studied in the setting of concomitant ambulatory blood pressure monitoring (ABPM) and Holter ECG monitoring (HM).Methods.In 25 hypertensive patients (14 women and 11 men, mean age: 58.1 years), 24-hour combined ABPM and HM were performed. For every blood pressure measurement, 2-minute ECG segments (before, during, and after measurement) were analyzed to obtain time domain parameters of HRV: SDNN and rMSSD. Mean of normal RR intervals (MNN), SDNN/MNN, and rMSSD/MNN were calculated, too. Parameter variations related to blood pressure measurements were analyzed using one-way ANOVA with multiple comparisons.Results.2281 measurements (1518 during the day and 763 during the night) were included in the analysis. Both SDNN and SDNN/MNN had a constant (the same for 24-hour, daytime, and nighttime values) and significant change related to blood pressure measurements: an increase during measurements and a decrease after them (p<0.01for any variation).Conclusion.In the setting of combined ABPM and HM, the blood pressure measurement itself produces an increase in short-term heart rate variability. Clarifying the physiological basis and the possible clinical value of this phenomenon needs further studies.

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