scholarly journals Progressive Dynamic Time Warping for Noninvasive Blood Pressure Estimation

2020 ◽  
Vol 6 (3) ◽  
pp. 579-582
Author(s):  
Alexandru-Gabriel Pielmus ◽  
Michael Klum ◽  
Timo Tigges ◽  
Reinhold Orglmeister ◽  
Mike Urban
Keyword(s):  
Blood Pressure ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Physical Strain ◽  
Test Protocol ◽  
Arterial Blood ◽  
Pulse Waves ◽  
Pressure Estimation ◽  
Dynamic Time ◽  
Blood Pressure Estimation

AbstractArterial blood pressure is one of the most important cardiovascular parameters. Yet, current-generation devices for continuous, noninvasive acquisition are few, expensive and bulky. Novel signal processing applied to easily acquired unimodal signals can alleviate this issue, reducing size, cost and expanding the use of such devices to ambulatory, everyday settings. The features of pulse waves acquired by photo- or impedance-plethysmography can be used to estimate the underlying blood pressure. We present a progressive dynamic time warping algorithm, which implicitly parametrizes the morphological changes in these waves. This warping method is universally applicable to most pulse wave shapes, as it is largely independent of fiducial point detection or explicit parametrization. The algorithm performance is validated in a feature selection and regression framework against a continuous, noninvasive Finapres NOVA monitor, regarding systolic, mean and diastolic pressures during a light physical strain test protocol on four clinically healthy subjects (age18- 33, one female). The obtained mean error is 2.13 mmHg, the mean absolute error is 5.4 mmHg and the standard deviation is 5.6 mmHg. These results improve on our previous work on dynamic time warping. Using single-sensor, peripherally acquired pulse waves, progressive dynamic time warping can thus improve the flexibility of noninvasive, continuous blood pressure estimation.

scholarly journals Dynamic Time Warping of Pulse Wave Curves

2018 ◽  
Vol 4 (1) ◽  
pp. 371-374 ◽  
Author(s):  
Alexandru-Gabriel Pielmuş ◽  
Dennis Osterland ◽  
Timo Tigges ◽  
Michael Klum ◽  
Reinhold Orglmeister ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Dynamic Time Warping ◽  
Pulse Wave ◽  
Morphological Changes ◽  
Applanation Tonometry ◽  
Small Subset ◽  
Time Warping ◽  
Absolute Deviation ◽  
Arterial Blood ◽  
Dynamic Time

AbstractBeing able to non-obtrusively and continuously monitor arterial blood pressure is of great interest, particularly in the context of wearable sensors. A common limitation is the need for dedicated hardware, which is either obtrusive or expensive. In our current work, we investigate unimodal pulse waves from three handily accessible heterogeneous sources: photoplethysmography, bioimpedance and pulse applanation tonometry. We derive and evaluate multiple parameters regarding their correlation to reference blood pressure curves. These parameters stem from features of the warping paths resulting from dynamic and derivative dynamic time warping. The warping is performed between adjacent pulses or to a reference waveform. Spearman Rho coefficients of up to 0.98 and averaging 0.77 at highly significant p-values are recorded for single parameters. We record mean absolute deviation values of 0.08 across subjects. The results indicate there are negligible lags between reference and parameter curves. The sign of the correlation coefficients is consistent only for a small subset of parameters; the underlying cause could not yet be identified. We conclude that the warping path approach seems a promising way to go, yet still needs refinement. In particular, developing a time and amplitude warping method is paramount. Since warping quantizes all the morphological changes in the pulse wave without fiducial point detection, it could become a powerful tool for future investigations.

scholarly journals Continuous Blood Pressure Estimation Using Exclusively Photopletysmography by LSTM-Based Signal-to-Signal Translation

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2952
Author(s):  
Latifa Nabila Harfiya ◽  
Ching-Chun Chang ◽  
Yung-Hui Li
Keyword(s):  
Blood Pressure ◽  
Absolute Error ◽  
Estimation Methods ◽  
British Hypertension Society ◽  
Arterial Blood ◽  
Proposed Model ◽  
Pressure Estimation ◽  
Effective Signal ◽  
Blood Pressure Estimation ◽  
Continuous Blood Pressure

Monitoring continuous BP signal is an important issue, because blood pressure (BP) varies over days, minutes, or even seconds for short-term cases. Most of photoplethysmography (PPG)-based BP estimation methods are susceptible to noise and only provides systolic blood pressure (SBP) and diastolic blood pressure (DBP) prediction. Here, instead of estimating a discrete value, we focus on different perspectives to estimate the whole waveform of BP. We propose a novel deep learning model to learn how to perform signal-to-signal translation from PPG to arterial blood pressure (ABP). Furthermore, using a raw PPG signal only as the input, the output of the proposed model is a continuous ABP signal. Based on the translated ABP signal, we extract the SBP and DBP values accordingly to ease the comparative evaluation. Our prediction results achieve average absolute error under 5 mmHg, with 70% confidence for SBP and 95% confidence for DBP without complex feature engineering. These results fulfill the standard from Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) with grade A. From the results, we believe that our model is applicable and potentially boosts the accuracy of an effective signal-to-signal continuous blood pressure estimation.

scholarly journals Noninvasive blood pressure estimation with peak delay of different pulse waves

2019 ◽  
Vol 15 (3) ◽  
pp. 155014771983787 ◽  
Author(s):  
Yibin Li ◽  
Shengnan Li ◽  
Houbing Song ◽  
Bin Shao ◽  
Xiao Yang ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Smooth Muscle ◽  
Phase Difference ◽  
Blood Pressure Monitoring ◽  
Study Phase ◽  
Noninvasive Blood Pressure ◽  
Hemodynamic Model ◽  
Pulse Waves ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

In this article, we discuss the validity of noninvasive continuous blood pressure estimation with two different types of peripheral pulse waves. Artery-blocking experiment shows that phase difference of two pulse waves at the same location is well related with blood pressure and blood flow fluctuation. Exercise-recovery experiment resulting from 16 subjects shows that phase difference varies with blood pressure with the correlation from 0.63 to 0.88 when blood pressure changes rapidly. Simulations based on a classic hemodynamic model verify the relationship between phase difference and blood pressure. However, phase difference is strongly correlated with smooth muscle state of the arterial wall as well. If smooth muscle information can be obtained by further study, phase difference can act as a promising approach to portable and wearable device for real-time blood pressure monitoring.

scholarly journals A Novel Clustering-Based Algorithm for Continuous and Noninvasive Cuff-Less Blood Pressure Estimation

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Ali Farki ◽  
Reza Baradaran Kazemzadeh ◽  
Elham Akhondzadeh Noughabi
Keyword(s):  
Blood Pressure ◽  
Clustering Algorithm ◽  
Machine Learning Algorithms ◽  
Weighted Averaging ◽  
Gradient Boosting ◽  
Data Set ◽  
Arterial Blood ◽  
Dependent Variables ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

Extensive research has been performed on continuous and noninvasive cuff-less blood pressure (BP) measurement using artificial intelligence algorithms. This approach involves extracting certain features from physiological signals, such as ECG, PPG, ICG, and BCG, as independent variables and extracting features from arterial blood pressure (ABP) signals as dependent variables and then using machine-learning algorithms to develop a blood pressure estimation model based on these data. The greatest challenge of this field is the insufficient accuracy of estimation models. This paper proposes a novel blood pressure estimation method with a clustering step for accuracy improvement. The proposed method involves extracting pulse transit time (PTT), PPG intensity ratio (PIR), and heart rate (HR) features from electrocardiogram (ECG) and photoplethysmogram (PPG) signals as the inputs of clustering and regression, extracting systolic blood pressure (SBP) and diastolic blood pressure (DBP) features from ABP signals as dependent variables, and finally developing regression models by applying gradient boosting regression (GBR), random forest regression (RFR), and multilayer perceptron regression (MLP) on each cluster. The method was implemented using the MIMIC-II data set with the silhouette criterion used to determine the optimal number of clusters. The results showed that because of the inconsistency, high dispersion, and multitrend behavior of the extracted features vectors, the accuracy can be significantly improved by running a clustering algorithm and then developing a regression model on each cluster and finally weighted averaging of the results based on the error of each cluster. When implemented with 5 clusters and GBR, this approach yielded an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were significantly better than the best results without clustering (DBP: 6.27, SBP: 6.36).

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