Pulse Arrival Time Segmentation Into Cardiac and Vascular Intervals - Implications for Pulse Wave Velocity and Blood Pressure Estimation

Garcia-Gonzalez M, Fernandez-Chimeno M, Guede-Fernández F, Mateu-Mateus M, Capdevila L, J. Ramos-Castro J. On the use of fractional calculus to improve the pulse arrival time (PAT) detection when using photoplethysmography (PPG) and electrocardiography (ECG) signals

The pulse arrival time (PAT) has been considered a surrogate measure for pulse wave velocity (PWV), although some studies have noted that this parameter is not accurate enough. Moreover, the inter-beat interval (IBI) time series obtained from successive pulse wave arrivals can be employed as a surrogate measure of the RR time series avoiding the use of electrocardiogram (ECG) signals. Pulse arrival detection is a procedure needed for both PAT and IBI measurements and depends on the proper fiducial points chosen. In this paper, a new set of fiducial points that can be tailored using several optimization criteria is proposed to improve the detection of successive pulse arrivals. This set is based on the location of local maxima and minima in the systolic rise of the pulse wave after fractional differintegration of the signal. Several optimization criteria have been proposed and applied to high-quality recordings of a database with subjects who were breathing at different rates while sitting or standing. When a proper fractional differintegration order is selected by using the RR time series as a reference, the agreement between the obtained IBI and RR is better than that for other state-of-the-art fiducial points. This work tested seven different traditional fiducial points. For the agreement analysis, the median standard deviation of the difference between the IBI and RR time series is 5.72 ms for the proposed fiducial point versus 6.20 ms for the best-performing traditional fiducial point, although it can reach as high as 9.93 ms for another traditional fiducial point. Other optimization criteria aim to reduce the standard deviation of the PAT (7.21 ms using the proposed fiducial point versus 8.22 ms to 15.4 ms for the best- and worst-performing traditional fiducial points) or to minimize the standard deviation of the PAT attributable to breathing (3.44 ms using the proposed fiducial point versus 4.40 ms to 5.12 ms for best- and worst-performing traditional fiducial points). The use of these fiducial points may help to better quantify the beat-to-beat PAT variability and IBI time series.

Assessment of aortic to peripheral vascular stiffness and gradient by segmented upper limb PWV in healthy and hypertensive individuals

Theoretically pulse wave velocity (PWV) is obtained by calculating the distance between two waveform probes divided by the time difference, and PWV ratio is used to assess the arterial stiffness gradient (SG) from proximal to distal. The aim was to investigate segmental upper-limb PWV (ulPWV) differences and the effects of hypertension and or aging on each ulPWV and SG. The study collected multi-waveform signals and conduction distances from 167 healthy individuals and 92 hypertensive patients. The results showed significant differences between ulPWVs (P < 0.001), with increased and then decreased vascular stiffness along the proximal transmission to the distal peripheral artery and then to the finger. Adjusted for age and sex, ulPWVs in hypertension exceeded that of healthy individuals, with significant differences between groups aged ≥ 50 years (P < 0.05). The hrPWV/rfPWV (heart-radial/radial-finger) was reduced in hypertension and differed significantly between the aged ≥ 50 years (P = 0.015); the ratio of baPWV (brachial-ankle) to ulPWV differed significantly between groups (P < 0.05). Hypertension affected the consistency of rfPWV with hfPWV (heart-finger). The findings suggest that segmented ulPWV is instrumental in providing stiffness corresponding to the physiological structure of the vessel. The superimposition of hypertension and or aging exacerbates peripheral arterial stiffness, as well as alteration in stiffness gradient.

Schrödinger spectrum based continuous cuff-less blood pressure estimation using clinically relevant features from PPG signal and its second derivative

The presented study estimates cuff-less blood pressure (BP) from photoplethysmography (PPG) signals using multiple machine-learning (ML) models and the semi-classical signal analysis (SCSA) technique. The study proposes a novel signal reconstruction algorithm, which optimizes the semi-classical constant of the SCSA approach and eliminates the trade-off between complexity and accuracy during signal reconstruction. It predicts BP values using regression algorithms by processing reconstructed PPG signals' spectral features, extracting clinically relevant PPG and its second derivative's (SDPPG) morphological features. The developed method was assessed using a virtual in-silico dataset with more than 4000 subjects and the Multi-Parameter Intelligent Monitoring in Intensive Care Units (MIMIC-II) dataset. Results showed that the method attained a mean absolute error (MAE) of 5.37 and 2.96 mmHg for systolic and diastolic BP, respectively, using the CatBoost algorithm. This approach met the Association for the Advancement of Medical Instrumentation's standard and achieved Grade A for all BP categories in the British Hypertension Society protocol. The proposed framework performs well even when applied to a combined clinically relevant database originating from MIMIC-III and the Queensland dataset. The proposed method's performance is further evaluated in a non-clinical setting with noisy and deformed PPG signals to validate the efficacy of the SCSA method. The noise stress tests further showed that the algorithm maintained its key feature detection, signal reconstruction capability, and estimation accuracy up to a 10 dB SNR ratio. The proposed cuff-less BP estimation technique has the potential to perform well in mobile healthcare devices due to its straightforward implementation approach.

New Hemodynamic Parameters in Peri-Operative and Critical Care-Challenges in Translation

Hemodynamic monitoring technologies are evolving continuously-a large number of bedside monitoring options are becoming available in the clinic. Methods such as echocardiography, electrical bioimpedance, and calibrated/uncalibrated analysis of pulse contours are becoming increasingly common. This is leading to a decline in the use of highly invasive monitoring and allowing for safer, more accurate, and continuous measurements. The new devices mainly aim to monitor the well-known hemodynamic variables (e.g., novel pulse contour, bioreactance methods are aimed at measuring widely-used variables such as blood pressure, cardiac output). Even though hemodynamic monitoring is now safer and more accurate, a number of issues remain due to the limited amount of information available for diagnosis and treatment. Extensive work is being carried out in order to allow for more hemodynamic parameters to be measured in the clinic. In this review, we identify and discuss the main sensing strategies aimed at obtaining a more complete picture of the hemodynamic status of a patient, namely: (i) measurement of the circulatory system response to a defined stimulus; (ii) measurement of the microcirculation; (iii) technologies for assessing dynamic vascular mechanisms; and (iv) machine learning methods. By analyzing these four main research strategies, we aim to convey the key aspects, challenges, and clinical value of measuring novel hemodynamic parameters in critical care.

Cerebral blood flow pulsatility and cerebral artery stiffness acutely decrease during hemodialysis

End-stage kidney disease (ESKD) is associated with increased arterial stiffness and cognitive impairment. Cognitive decline is accelerated in ESKD patients on hemodialysis and may result from repeatedly inappropriate cerebral blood flow (CBF). The aim of this study was to examine the acute effect of hemodialysis on pulsatile components of CBF and their relation to acute changes in arterial stiffness. In eight participants (age: 63 ± 18 years, men: 5), CBF was estimated using middle cerebral artery blood velocity (MCAv) assessed with transcranial Doppler ultrasound before, during, and after a single hemodialysis session. Brachial and central blood pressure, along with estimated aortic stiffness (eAoPWV) were measured using an oscillometric device. Arterial stiffness from heart to MCA was measured as the pulse arrival time (PAT) between electrocardiogram (ECG) and transcranial Doppler ultrasound waveforms (cerebral PAT). During hemodialysis, there was a significant reduction in mean MCAv (-3.2 cm/s, p < 0.001), and systolic MCAv (-13.0 cm/s, p < 0.001). While baseline eAoPWV (9.25 ± 0.80 m/s) did not significantly change during hemodialysis, cerebral PAT increased significantly (+0.027 , p < 0.001) and was associated with reduced pulsatile components of MCAv. This study shows that hemodialysis acutely reduces stiffness of arteries perfusing the brain along with pulsatile components of blood velocity.

The B-Score is a novel metric for measuring the true performance of blood pressure estimation models

We aimed to develop and test a novel metric for the relative performance of blood pressure estimation systems (B-Score). The B-Score sets absolute blood pressure estimation model performance in contrast to the dataset the model is tested upon. We calculate the B-Score based on inter- and intrapersonal variabilities within the dataset. To test the B-Score for reliable results and desired properties, we designed generic datasets with differing inter- and intrapersonal blood pressure variability. We then tested the B-Score’s real-world functionality with a small, published dataset and the largest available blood pressure dataset (MIMIC IV). The B-Score demonstrated reliable and desired properties. The real-world test provided allowed the direct comparison of different datasets and revealed insights hidden from absolute performance measures. The B-Score is a functional, novel, and easy to interpret measure of relative blood pressure estimation system performance. It is easily calculated for any dataset and enables the direct comparison of various systems tested on different datasets. We created a metric for direct blood pressure estimation system performance. The B-Score allows researchers to detect promising trends quickly and reliably in the scientific literature. It further allows researchers and engineers to quickly assess and compare performances of various systems and algorithms, even when tested on different datasets.

A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model

Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill 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 model can effectively estimate the BP waveform only using the raw PPG signal.

Cuff-less blood pressure measurement based on hybrid feature selection algorithm and multi-penalty regularized regression technique

One of the prominent reasons behind the deterioration of cardiovascular conditions is hypertension. Due to lack of specific symptoms, sometimes existing hypertension goes unnoticed until significant damage happens to the heart or any other body organ. Monitoring of BP at a higher frequency is necessary so that we can take early preventive measures to control and keep it within the normal range. The cuff-based method of measuring BP is inconvenient for frequent daily measurements. The cuffless BP measurement method proposed in this paper uses features extracted from the electrocardiogram (ECG) and photoplethysmography (PPG). ECG and PPG both have distinct characteristics, which change with the change of blood pressure levels. Feature extraction and hybrid feature selection algorithms are followed by a generalized penalty-based regression technique led to a new BP measurement process that uses the minimum number of features. The performance of the proposed technique to measure blood pressure was compared to an approach using an ordinary linear regression method with no feature selection and to other contemporary techniques. MIMIC-II database was used to train and test our proposed method. The root mean square error (RMSE) for systolic blood pressure (SBP) improved from 11.2 mmHg to 5.6 mmHg when the proposed technique was implemented and for diastolic blood pressure (DBP) improved from 12.7 mmHg to 6.69 mmHg. The mean absolute error (MAE) was found to be 4.91 mmHg for SBP and 5.77 mmHg for DBP, which have shown improvement over other existing cuffless techniques where the substantial number of patients, as well as feature selection algorithm, were implemented. In addition, according to the British Hypertension Society standard (BHS) standard for cuff-based BP measurement, the criteria for acceptable measurement are to achieve at least grade B; our proposed method also satisfies this criterion.