scholarly journals A Unified Calibration Paradigm for a Better Cuffless Blood Pressure Estimation with Modes of Elastic Tube and Vascular Elasticity

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jiang Shao ◽  
Ping Shi ◽  
Sijung Hu
Keyword(s):  
Blood Pressure ◽  
Elastic Tube ◽  
Least Square ◽  
Calibration Methods ◽  
Significant Difference ◽  
Vascular Elasticity ◽  
Point To Point ◽  
Blood Pressure Estimation ◽  
Cuffless Blood Pressure ◽  
Selection Of

Although two modes of elastic tube (ET) and vascular elasticity (VE) have been well explored for cuffless continuous blood pressure (BP) monitoring estimation, the initial calibration with these two models could be derived from different mathematical mechanisms for BP estimation. The study is aimed at evaluating the performance of VE and ET models by means of an advanced point-to-point (aPTP) pairing calibration. The cuff BPs were only taken up while the signals of PPG and ECG were synchronously acquired from individual subjects. Two popular VE models together with one representative ET model were designated to study aPTP as a unified assessment criterion. The VE model has demonstrated the stronger correlation r of 0.89 and 0.86 of SBP and DBP, respectively, and the lower estimated BP error of − 0.01 ± 5.90 (4.55) mmHg and 0.04 ± 4.40 (3.38) mmHg of SBP and DBP, respectively, than the ET model. With the ET model, there is a significant difference between the methods of conventional least-square (LS) calibration and aPTP calibration ( p < 0.05 ). These results showed that the VE model surpasses the ET model under the same uniform calibration. The outcome has been unveiled that the selection of initial calibration methods was vital to work out diastolic BP with the ET model. The study revealed an evident fact about initial sensitivity between the modes of different BP estimation and initial calibration.

scholarly journals An Optimization Study of Estimating Blood Pressure Models Based on Pulse Arrival Time for Continuous Monitoring

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Jiang Shao ◽  
Ping Shi ◽  
Sijung Hu ◽  
Yang Liu ◽  
Hongliu Yu
Keyword(s):  
Blood Pressure ◽  
Mathematical Expression ◽  
Elastic Tube ◽  
Good Choice ◽  
Estimation Model ◽  
Ecg Signals ◽  
Optimization Study ◽  
Calibration Methods ◽  
Point To Point ◽  
Estimated Error

Continuous blood pressure (BP) monitoring has a significant meaning for the prevention and early diagnosis of cardiovascular disease. However, under different calibration methods, it is difficult to determine which model is better for estimating BP. This study was firstly designed to reveal a better BP estimation model by evaluating and optimizing different BP models under a justified and uniform criterion, i.e., the advanced point-to-point pairing method (PTP). Here, the physical trial in this study caused the BP increase largely. In addition, the PPG and ECG signals were collected while the cuff bps were measured for each subject. The validation was conducted on four popular vascular elasticity (VE) models (MK-EE, L-MK, MK-BH, and dMK-BH) and one representative elastic tube (ET) model, i.e., M-M. The results revealed that the VE models except for L-MK outperformed the ET model. The linear L-MK as a VE model had the largest estimated error, and the nonlinear M-M model had a weaker correlation between the estimated BP and the cuff BP than MK-EE, MK-BH, and dMK-BH models. Further, in contrast to L-MK, the dMK-BH model had the strongest correlation and the smallest difference between the estimated BP and the cuff BP including systolic blood pressure (SBP) and diastolic blood pressure (DBP) than others. In this study, the simple MK-EE model showed the best similarity to the dMK-BH model. There were no significant changes between MK-EE and dMK-BH models. These findings indicated that the nonlinear MK-EE model with low estimated error and simple mathematical expression was a good choice for application in wearable sensor devices for cuff-less BP monitoring compared to others.

scholarly journals A Comparative Study on Instantaneous and Mean Pulse Arrival Time for Cuffless Blood Pressure Estimation

2021 ◽  
Vol 2071 (1) ◽  
pp. 012028
Author(s):  
W S Wan Zaki ◽  
R Correia ◽  
S Korposh ◽  
B R Hayes-Gill ◽  
S P Morgan
Keyword(s):  
Blood Pressure ◽  
Arrival Time ◽  
Blood Pressure Measurement ◽  
Single Pulse ◽  
Pulse Arrival Time ◽  
Significant Difference ◽  
Acquisition Device ◽  
Blood Pressure Estimation ◽  
Cuffless Blood Pressure ◽  
Pulse Arrival

Abstract Pulse arrival time (PAT) is the delay time between the peak of the R-wave Electrocardiogram (ECG) signal and the peak of Photoplethysmogram (PPG) signals. This method is widely exploited for continuous cuffless blood pressure measurement. In the literature, the PAT was determined based on the mean at a certain number or certain period of heartbeats, but none of them deployed a single pulse wave for PAT calculation. Therefore, in this paper, a relationship between mean PAT (15 pulses ± Standard Deviation (SD)) and instantaneous PAT (a pulse) with blood pressure (BP) was investigated on thirteen healthy male volunteers (aged between 17 to 42 years) through a pedal exercise. The PAT is grouped into three (3) categories which depend on the spatial position of the PPG signal measured; finger (PATf), wrist (PATw), and underfoot (PATt). The ECG and the PPG signals were synchronized using a Nexus-10 MK II data acquisition device and Matlab software (R 2014b) for subsequent analysis. An oscillometric cuff-based blood pressure instrument (Ostar, P2) was used as a BP reference during the experiment. Statistical analysis showed no significant difference in the |r| value between mean (15 pulses ± SD) and instantaneous PAT-BP; hence both methods are applicable for BP estimation using the PAT-BP calibration technique.

scholarly journals Concatenated Convolutional Neural Network Model for Cuffless Blood Pressure Estimation Using Fuzzy Recurrence Properties of PPG Signals

2022 ◽  
Author(s):  
Ali Bahari Malayeri ◽  
Mohammad Bagher Khodabakhshi
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Neural Networks ◽  
Network Architecture ◽  
Spatial Information ◽  
Recurrence Plot ◽  
Estimation Model ◽  
British Hypertension Society ◽  
Blood Pressure Estimation ◽  
Cuffless Blood Pressure

Abstract Due to the importance of continuous monitoring of blood pressure (BP) in controlling hypertension, the topic of cuffless blood pressure (BP) estimation has been widely studied in recent years. A most important approach is to explore the nonlinear mapping between the recorded peripheral signals and the BP values which is usually conducted by deep neural networks. Because of the sequence-based pseudo periodic nature of peripheral signals such as photoplethysmogram (PPG), a proper estimation model needed to be equipped with the 1-dimensional (1-D) and recurrent layers. This, in turn, limits the usage of 2-dimensional (2-D) layers adopted in convolutional neural networks (CNN) for embedding spatial information in the model. In this study, considering the advantage of chaotic approaches, the recurrence characterization of peripheral signals was taken into account by a visual 2-D representation of PPG in phase space through fuzzy recurrence plot (FRP). FRP not only provides a beneficial framework for capturing the spatial properties of input signals but also creates a reliable approach for embedding the pseudo periodic properties to the neural models without using recurrent layers. Moreover, this study proposes a novel deep neural network architecture that combines the morphological features extracted simultaneously from two upgraded 1-D and 2-D CNNs capturing the temporal and spatial dependencies of PPGs in systolic and diastolic BP estimation. The model has been fed with the 1-D PPG sequences and the corresponding 2-D FRPs from two separate routes. The performance of the proposed framework was examined on the well-known public dataset, namely, Multi-Parameter Intelligent in Intensive Care II. Our scheme is analyzed and compared with the literature in terms of the requirements of the standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The proposed model met the AAMI requirements, and it achieved a grade of A as stated by the BHS standard. In addition, its mean absolute errors (MAE) and standard deviation for both systolic and diastolic blood pressure estimations were considerably low, 3.05±5.26 mmHg and 1.58±2.6 mmHg, in turn.

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