Pulse Transit Time and Blood Pressure During Cardiopulmonary Exercise Tests

Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study

Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this procedure is invasive and time-consuming to the extent that physicians rather rely on non-invasive diagnostic tools. In this work, we assess the feasibility to develop a novel machine-learning (ML) approach to predict clinically relevant LVP indices. Synchronized invasive (pressure-volume tracings) and non-invasive signals (ECG, pulse oximetry, and cardiac sounds) were collected from anesthetized, closed-chest Göttingen minipigs. Animals were either healthy or had HF with reduced ejection fraction and circa 500 heartbeats were included in the analysis for each animal. The ML algorithm showed excellent prediction of LVP indices estimating, for instance, the end-diastolic pressure with a R2 of 0.955. This novel ML algorithm could assist clinicians in the care of HF patients.

Recent Advances in Materials, Devices and Algorithms Toward Wearable Continuous Blood Pressure Monitoring

Continuous blood pressure (BP) tracking provides valuable insights into the health condition and functionality of the heart, arteries, and overall circulatory system of humans. The rapid development in flexible and wearable electronics has significantly accelerated the advancement of wearable BP monitoring technologies. However, several persistent challenges, including limited sensing capabilities and stability of flexible sensors, poor interfacial stability between sensors and skin, and low accuracy in BP estimation, have hindered the progress in wearable BP monitoring. To address these challenges, comprehensive innovations in materials design, device development, system optimization, and modeling have been pursued to improve the overall performance of wearable BP monitoring systems. In this review, we highlight the latest advancements in flexible and wearable systems toward continuous noninvasive BP tracking with a primary focus on materials development, device design, system integration, and theoretical algorithms. Existing challenges, potential solutions, and further research directions are also discussed to provide theoretical and technical guidance for the development of future wearable systems in continuous ambulatory BP measurement with enhanced sensing capability, robustness, and long-term accuracy.

Calibration-free blood pressure estimation based on a convolutional neural network

In this study, we conducted research on a deep learning-based blood pressure (BP) estimation model suitable for wearable environments. To measure BP while wearing a wearable watch, it needs to be considered that computing power for signal processing is limited and the input signals are subject to noise interference. Therefore, we employed a convolutional neural network (CNN) as the BP estimation model and utilized time-series electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which are quantifiable in a wearable context. We generated periodic input signals and used differential and thresholding methods to decrease noise in the preprocessing step. We then applied a max-pooling technique with filter sizes of 2 × 1 and 5 × 1 within a 3-layer convolutional neural network to estimate BP. Our method was trained, validated, and tested using 2.4 million data samples from 49 patients in the intensive care unit. These samples, totaling 3.1 GB were obtained from the publicly accessible MIMIC database. As a result of a test with 480,000 data samples, the average root mean square error in BP estimation was 3.41, 5.80, and 2.78 mm Hg in the prediction of pulse pressure, systolic BP (SBP), and diastolic BP (DBP), respectively. The cumulative error percentage less than 5 mm Hg was 68% and 93% for SBP and DBP, respectively. In addition, the cumulative error percentage less than 15 mm Hg was 98% and 99% for SBP and DBP. Subsequently, we evaluated the impact of changes in input signal length (1 cycle vs. 30 s) and the introduction of noise on BP estimation results. The experimental results revealed that the length of the input signal did not significantly affect the performance of CNN-based analysis. When estimating BP using noise-added ECG signals, the mean absolute error (MAE) for SBP and DBP was 9.72 and 6.67 mm Hg, respectively. Meanwhile, when using noise-added PPG signals, the MAE for SBP and DBP was 26.85 and 14.00 mm Hg, respectively. Therefore, this study confirmed that using ECG signals rather than PPG signals is advantageous for noise reduction in a wearable environment. Besides, short sampling frames without calibration can be effective as input signals. Furthermore, it demonstrated that using a model suitable for information extraction rather than a specialized deep learning model for sequential data can yield satisfactory results in BP estimation.

Meta-Analysis of Pulse Transition Features in Non-Invasive Blood Pressure Estimation Systems: Bridging Physiology and Engineering Perspectives

The pulse transition features (PTFs), including pulse arrival time (PAT) and pulse transition time (PTT), hold significant importance in estimating non-invasive blood pressure (NIBP). However, the literature showcases considerable variations in terms of PTFs' correlation with blood pressure (BP), accuracy in NIBP estimation, and the comprehension of the relationship between PTFs and BP. This inconsistency is exemplified by the wide-ranging correlations reported across studies investigating the same feature. Furthermore, investigations comparing PAT and PTT have yielded conflicting outcomes. Additionally, PTFs have been derived from various bio-signals, capturing distinct characteristic points like the pulse's foot and peak. To address these inconsistencies, this study meticulously reviews a selection of such research endeavors while aligning them with the biological intricacies of blood pressure and the human cardiovascular system (CVS). Each study underwent evaluation, considering the specific signal acquisition locale and the corresponding recording procedure. Moreover, a comprehensive meta-analysis was conducted, yielding multiple conclusions that could significantly enhance the design and accuracy of NIBP systems. Grounded in these dual aspects, the study systematically examines PTFs in correlation with the specific study conditions and the underlying factors influencing the CVS. This approach serves as a valuable resource for researchers aiming to optimize the design of BP recording experiments, bio-signal acquisition systems, and the fine-tuning of feature engineering methodologies, ultimately advancing PTF-based NIBP estimation.

Analysis for calibration pre-post difference in BP estimation of Galaxy Watch

The use of smartwatches has become increasingly common with the release of major products such as the Galaxy Watch by Samsung and the Apple Watch by Apple. The common aim of smartwatches is to target the healthcare market with a wearable, physically-attached device, with blood pressure at the core. As blood pressure is an important biomarker for cardiovascular-related diseases, it is a necessary index to inspect in hospitals when checking an individual's health state. Smartwatches are expected to provide a cuff-less, non-invasive method of estimating blood pressure. However, not many experiments have been conducted on blood pressure datasets obtained from smartwatches. Smartwatches are unique compared to other devices because they require "calibration" to sustain their accuracy.In this paper, we investigate the difference between before and after calibration to better understand the calibration pattern. Not only do we seek to understand the demographic differences in calibration, but we also analyze the possible variables that influence calibration differences. Our results show that hypertensive patients are more prone to high calibration differences, which implies that the calibration period should be adjusted by considering the average blood pressure of users.Clinical Relevance- This paper investigates the possibility for daily BP measurement to be used as clinical data while suggesting proper method to sustain its validity.

Investigation of Optimal Light Source Wavelength for Cuffless Blood Pressure Estimation Using a Single Photoplethysmography Sensor

Routine blood pressure measurement is important for the early detection of various diseases. Recently, cuffless blood pressure estimation methods that do not require cuff pressurization have attracted attention. In this study, we investigated the effect of the light source wavelength on the accuracy of blood pressure estimation using only two physiological indices that can be calculated with photoplethysmography alone, namely, heart rate and modified normalized pulse volume. Using a newly developed photoplethysmography sensor that can simultaneously measure photoplethysmograms at four wavelengths, we evaluated its estimation accuracy for systolic blood pressure, diastolic blood pressure, and mean arterial pressure against a standard cuff sphygmomanometer. Mental stress tasks were used to alter the blood pressure of 14 participants, and multiple linear regression analysis showed the best light sources to be near-infrared for systolic blood pressure and blue for both diastolic blood pressure and mean arterial pressure. The importance of the light source wavelength for the photoplethysmogram in cuffless blood pressure estimation was clarified.

A novel method for non-invasive blood pressure estimation based on continuous pulse transit time: An observational study

Unlike traditional pulse transit time (PTT), continuous PTT (CPTT) can be used to calculate PTT from all samples within the cardiac cycle. It has the potential to be utilized for continuous blood pressure (BP) estimation. This study evaluated the feasibility of CPTT as a non-invasive consecutive blood pressure estimation method in 20 volunteers. The CPTT was calculated with a time delay in all discrete samples of photoplethysmograms measured at two different body sites. BP was then calculated with a regression equation. For comparative evaluation, BP based on PTT was also estimated. Continuous blood pressure was measured using a non-invasive volume clamp BP monitoring device. Four types of BP measurement, systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP), were estimated using PTT and CPTT. Correlation coefficients and root-mean-squared-error (RMSE) were used for evaluating BP estimation performance. For estimating SBP, DBP, PP, and MAP, PTT-based BP estimation showed correlations of .407, .373, .410, and .286, respectively, and CPTT-based BP estimation showed correlations of .436, .446, .506, and .097, respectively. With PTT-based estimation, the RMSE between the estimated BP and the baseline BP was 5.44 ± 1.56 mmHg for SBP, 3.14 ± 0.46 mmHg for DBP, 3.66 ± 0.70 mmHg for MAP, and 3.73 ± 1.31 mmHg for PP. The estimated BP using CPTT showed RMSE of 5.36 ± 1.39 mmHg for SBP, 3.02 ± 0.49 mmHg for SBP, 3.44 ± 0.63 mmHg for MAP, and 3.91 ± 1.41 mmHg for PP.

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.

An Arterial Compliance Sensor for Cuffless Blood Pressure Estimation Based on Piezoelectric and Optical Signals

OBJECTIVE

Blood pressure (BP) data can influence therapeutic decisions for some patients, while non-invasive devices that continuously monitor BP can provide patients with a more comprehensive BP assessment. Therefore, this study proposes a multi-sensor-based small cuffless BP monitoring device that integrates a piezoelectric sensor array and an optical sensor, which can monitor the patient's physiological signals from the radial artery.

METHOD

Based on the Moens-Korteweg (MK) equation of the hemodynamic model, pulse wave velocity (PWV) can be correlated with arterial compliance and BP can be estimated. Therefore, the novel method proposed in this study involves using a piezoelectric sensor array to measure the PWV and an optical sensor to measure the photoplethysmography (PPG) intensity ratio (PIR) signal to estimate the participant's arterial parameters. The parameters measured by multiple sensors were combined to estimate BP based on the P-β model derived from the MK equation.

RESULT

We recruited 20 participants for the BP monitoring experiment to compare the performance of the BP estimation method with the regression model and the P-β model method with arterial compliance. We then compared the estimated BP with a reference device for validation. The results are presented as the error mean ± standard deviation (SD). Based on the regression model method, systolic blood pressure (SBP) was 0.32 ± 5.94, diastolic blood pressure (DBP) was 2.17 ± 6.22, and mean arterial pressure (MAP) was 1.55 ± 5.83. The results of the P-β model method were as follows: SBP was 0.75 ± 3.9, DBP was 1.1 ± 3.12, and MAP was 0.49 ± 2.82.

CONCLUSION

According to the results of our proposed small cuffless BP monitoring device, both methods of estimating BP conform to ANSI/AAMI/ISO 81060-2:20181_5.2.4.1.2 criterion 1 and 2, and using arterial parameters to calibrate the MK equation model can improve BP estimate accuracy. In the future, our proposed device can provide patients with a convenient and comfortable BP monitoring solution. Since the device is small, it can be used in a public place without attracting other people's attention, thereby effectively improving the patient's right to privacy, and increasing their willingness to use it.

Cuff-less and continuous blood pressure measurement based on pulse transit time from carotid and toe photoplethysmograms

Blood pressure (BP) is a vital health parameter that varies throughout the day. As a single reading of high BP may not indicate hypertension, continuous monitoring of BP is usually recommended by medical doctors to confirm the diagnosis of hypertension. In the last few decades, researchers have investigated cuff-less and continuous BP measurements based on pulse transit time (PTT). The main purpose of this research is to develop an autoregressive (ARX) system identification (SI)-based PTT calculation model using two PPG signals acquired from carotid and toe. The signals were recorded from 65 subjects with an age range between 20 and 60 years. The results of the study have been validated in two stages. The first validation comprised the estimated BP from PTT using SI compared to the measured BP using the cuff-based method for all subjects. The results of the estimated BP using the proposed method compared to the measured BP obtained using the standard BP cuff measurement method are highly correlated to both systolic blood pressure (R² = 0.8132) and diastolic blood pressure (R² = 0.8357). The second validation consisted of comparing PTT values using system identification to the results of the PTT derived from the ECG-PPG method. The results showed that both methods are highly correlated (R² = 0.7808), and there is no significant difference between them (p < 0.05) with a slightly better PTT estimation related to DBP in the proposed method. Our results have proven that the PTT obtained from the carotid PPG and toe PPG using the system identification approach yielded SBP and DBP estimations that are consistent with the values of the conventional BP cuff method. The newly proposed method has the advantage of being cuff-less and able to provide continuous BP measurements.

Blood pressure altering method affects correlation with pulse arrival time

OBJECTIVE

Pulse arrival time (PAT) is a potential main feature in cuff-less blood pressure (BP) monitoring. However, the precise relationship between BP parameters and PAT under varying conditions lacks a complete understanding. We hypothesize that simple test protocols fail to demonstrate the complex relationship between PAT and both SBP and DBP. Therefore, this study aimed to investigate the correlation between PAT and BP during two exercise modalities with differing BP responses using an unobtrusive wearable device.

METHODS

Seventy-five subjects, of which 43.7% had a prior diagnosis of hypertension, participated in an isometric and dynamic exercise test also including seated periods of rest prior to, in between and after. PAT was measured using a prototype wearable chest belt with a one-channel electrocardiogram and a photo-plethysmography sensor. Reference BP was measured auscultatory.

RESULTS

Mean individual correlation between PAT and SBP was -0.82 ± 0.14 in the full protocol, -0.79 ± 0.27 during isometric exercise and -0.77 ± 0.19 during dynamic exercise. Corresponding correlation between PAT and DBP was 0.25 ± 0.35, -0.74 ± 0.23 and 0.39 ± 0.41.

CONCLUSION

The results confirm PAT as a potential main feature to track changes in SBP. The relationship between DBP and PAT varied between exercise modalities, with the sign of the correlation changing from negative to positive between type of exercise modality. Thus, we hypothesize that simple test protocols fail to demonstrate the complex relationship between PAT and BP with emphasis on DBP.

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.

CNN-LSTM Prediction Method for Blood Pressure Based on Pulse Wave

Cardiovascular disease (CVD), which seriously threatens human health, can be prevented by blood pressure (BP) measurement. However, convenient and accurate BP measurement is a vital problem. Although the easily-collected pulse wave (PW)-based methods make it possible to monitor BP at all times and places, the current methods still require professional knowledge to process the medical data. In this paper, we combine the advantages of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to propose a CNN-LSTM BP prediction method based on PW data. In detailed, CNN first extract features from PW data, and then the features are input into LSTM for further training. The numerical results based on real-life data sets show that the proposed method can achieve high predicted accuracy of BP while saving training time. As a result, CNN-LSTM can achieve convenient BP monitoring in daily health.

Oxygen desaturation rate as a novel intermittent hypoxemia parameter in severe obstructive sleep apnea is strongly associated with hypertension

STUDY OBJECTIVES

To investigate the effects of different intermittent hypoxemia properties on blood pressure (BP) and short-term blood pressure variability (BPV) in severe obstructive sleep apnea (OSA) patients.

METHODS

Nocturnal BP was continuously monitored by measuring pulse transmit time. Apnea-related systolic BP elevation values were used to reflect BPV. Beat-to-beat R-R interval data were incorporated in polysomnography for heart rate variability analysis. The low-frequency/high-frequency band ratio was used to reflect sympathovagal balance. The rate of pulse oxyhemoglobin saturation (SpO₂) decrease was counted as the change in the percentage of SpO₂ per second after obstructive apnea and expressed as the oxygen desaturation rate (ODR). Patients with severe OSA (n = 102) were divided into 2 groups according to the median ODR: faster ODR (FODR group: ODR > 0.37, n = 50) and slower ODR (ODR ≤ 0.37, n = 52).

RESULTS

Comparisons between the 2 groups showed significantly higher systolic BP (SBP) values in the FODR group than in the slower ODR group (awake SBP 149.9 ± 18.3 vs 131.8 ± 15.6 mm Hg; asleep SBP: 149.6 ± 19.9 vs 128.7 ± 15.6 mm Hg; both P < .001), as well as short-term BPV (15.0 ± 4.8 vs 11.6 ± 3.6 mm Hg; P < .001), and the prevalence of hypertension (74.0% vs 26.9%; P < .001). Multiple linear regression analyses revealed that after adjusting for body mass index, functional residual capacity, expiratory reserve volume, and baseline SpO_2, ODR, as assessed by ΔSpO₂/Δt, had the strongest association with both BP and short-term BPV. Correlation analysis showed that ODR was positively correlated with the low-frequency/high-frequency band ratio (r = .288, P = .003).

CONCLUSIONS

ODR, as a novel hypoxemia profile, was more closely associated with the elevation of BP and BPV in patients with severe OSA. FODR might be associated with enhanced sympathetic activity.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: Characteristics of Obstructive Sleep Apnea Syndrome Related Hypertension and the Effect of Continuous Positive Airway Pressure Treatment on Blood Pressure; URL: https://clinicaltrials.gov/ct2/show/NCT03246022; Identifier: NCT03246022.

Study of cuffless blood pressure estimation method based on multiple physiological parameters

Objective.Noninvasive blood pressure (BP) measurement technologies have been widely studied, but they still have the disadvantages of low accuracy, the requirement for frequent calibration and limited subjects. This work considers the regulation of vascular activity by the sympathetic nervous system and proposes a method for estimating BP using multiple physiological parameters.Approach.The parameters used in the model consist of heart rate variability (HRV), pulse transit time (PTT) and pulse wave morphology features extracted from electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Through four classic machine learning algorithms, a hybrid data set of 3337 subjects from two databases is evaluated to verify the ability of cross-database migration. We also recommend an individual calibration procedure to further improve the accuracy of the method.Main results.The mean absolute error (MAE) and the root mean square error (RMSE) of the proposed algorithm is 10.03 and 14.55 mmHg for systolic BP (SBP), and 5.42 and 8.19 mmHg for diastolic BP (DBP). With individual calibration, the MAE and standard deviation (SD) is -0.16 ± 7.96 (SBP) and -0.13 ± 4.50 (DBP) mmHg, which satisfied the Advancement of Medical Instrumentation (AAMI) standard. In addition, the models are used to test single databases to evaluate their performance on different data sources. The overall performance of the Adaboost algorithm is better on the Multi-parameter Intelligent Monitoring in Intensive Care Unit (MIMIC) database; the MAE between its predicted value and true value reaches 6.6mmHg (SBP) and 3.12mmHg (DBP), respectively.Significance.The proposed method considers the regulation of blood vessels and the heart by the autonomic nervous system, and verifies its effectiveness and robustness across data sources, which is promising for improving the accuracy of continuous and cuffless BP estimation.

A Wearable Tele-Health System towards Monitoring COVID-19 and Chronic Diseases

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since early 2020. The coronavirus disease 2019 (COVID-19) has already caused more than two million deaths worldwide and affected people's physical and mental health. COVID-19 patients with mild symptoms are generally required to self-isolate and monitor for symptoms at least for 14 days in the case the disease turns towards severe complications. Here, we overviewed the impact of COVID-19 on the patients' general health with a focus on their cardiovascular, respiratory and mental health, and investigated several existing patient monitoring systems. We addressed the limitations of these systems and proposed a wearable telehealth solution for monitoring a set of physiological parameters that are critical for COVID-19 patients such as body temperature, heart rate, heart rate variability, blood oxygen saturation, respiratory rate, blood pressure, and cough. This physiological information can be further combined to potentially estimate the lung function using artificial intelligence (AI) and sensor fusion techniques. The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.

The rate of decrease in oxygen desaturation during severe obstructive sleep apnea syndrome is correlated with subjective excessive daytime sleepiness

PURPOSE

To explore factors that influence subjective excessive daytime sleepiness (EDS) in patients with severe obstructive sleep apnea syndrome (OSAS).

METHODS

Patients with snoring seen at the Sleep Medicine Center of The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University between October 2018 and November 2019 were included in this study. All patients underwent polysomnography (PSG). Noninvasive frequency-domain analysis was used to assess the autonomic nervous system regulation of the heart, with the low frequency (LF)/high frequency (HF) power ratio used to represent the sympathetic-parasympathetic balance. Daytime sleepiness was evaluated by the Epworth sleepiness scale (ESS). Overnight apnea episodes were included for analyses. The rate of pulse oxyhemoglobin saturation (SpO₂) decrease was measured as the change in the percentage of SpO₂ per second after obstructive apnea and was expressed as the oxygen desaturation rate (ODR).

RESULTS

A total of 101 patients with severe OSAS were enrolled in this study and were further divided into two groups: the EDS group (ESS > 10, n = 52) and the non-EDS group (ESS ≤ 10, n = 49). The apnea-hypopnea index (AHI), respiratory effort-related arousals (RERAs), and LF/HF power ratio were significantly higher in the EDS group than in the non-EDS group (AHI: 69.9 ± 14.5 vs. 57.9 ± 16.1 events/h; RERAs: 42.2 ± 16.7 vs. 30.4 ± 13.7 events/h; LF/HF power ratio: 2.9 ± 0.8% vs. 2.4 ± 0.9%, all p < 0.001). Multiple linear regression analyses revealed that after adjusting for covariates expected to affect this relationship, ESS scores were correlated with ODR (β = 0.520, p < 0.001) and LF/HF power ratio (β = 0.155, p = 0.028), rather than with the traditional sleep-disordered breathing parameters.

CONCLUSIONS

Compared with the traditional PSG parameters, both ODR and an increased LF/HF power ratio were more closely related to daytime sleepiness, especially ODR.

Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

Elevated blood pressure (BP) is a major cause of death, yet hypertension commonly goes undetected. Owing to its nature, it is typically asymptomatic until later in its progression when the vessel or organ structure has already been compromised. Therefore, noninvasive and continuous BP measurement methods are needed to ensure appropriate diagnosis and early management before hypertension leads to irreversible complications. Photoplethysmography (PPG) is a noninvasive technology with waveform morphologies similar to that of arterial BP waveforms, therefore attracting interest regarding its usability in BP estimation. In recent years, wearable devices incorporating PPG sensors have been proposed to improve the early diagnosis and management of hypertension. Additionally, the need for improved accuracy and convenience has led to the development of devices that incorporate multiple different biosignals with PPG. Through the addition of modalities such as an electrocardiogram, a final measure of the pulse wave velocity is derived, which has been proved to be inversely correlated to BP and to yield accurate estimations. This paper reviews and summarizes recent studies within the period 2010–2019 that combined PPG with other biosignals and offers perspectives on the strengths and weaknesses of current developments to guide future advancements in BP measurement. Our literature review reveals promising measurement accuracies and we comment on the effective combinations of modalities and success of this technology.

Pulse-wave transit time with ventilator-induced variation for the prediction of fluid responsiveness

Aim

Although pulse pressure variation is a good predictor of fluid responsiveness, its measurement is invasive. Therefore, a technically simple, non‐invasive method is needed for evaluating circulatory status to prevent fluid loading and optimize hemodynamic status. We focused in the pulse‐wave transit time (PWTT) defined as the time interval between electrocardiogram R wave to plethysmograph upstroke, which has been recently introduced to non‐invasively assess cardiovascular response. In the present study, we evaluated the efficacy of pulse‐wave transit time (PWTT) with ventilator‐induced variation (PWTTV) in predicting fluid responsiveness.

Methods

We evaluated six domestic pigs weighing 46.0 ± 3.5 kg. After anesthesia induction, electrocardiogram, femoral arterial blood pressure, plethysmograph on the tail, and carotid artery blood flow were monitored and hemorrhage was induced by withdrawing 20 mL/kg blood over 20 min; 5 mL/kg blood volume was then autotransfused over 10 min. Then PWTTV and pulse pressure variation were measured at tidal volumes of 6 and 12 mL/kg.

Results

Area under the receiver operating curve values for the prediction of a >10% change in carotid artery blood flow were 0.979 for pulse pressure variation and 0.993 for PWTTV at a tidal volume of 6 mL/kg and 0.979 and 0.979, respectively, at a tidal volume of 12 mL/kg (all P < 0.0001).

Conclusions

Measured non‐invasively, PWTTV showed similar utility to pulse pressure variation in predicting >10% changes in carotid artery blood flow induced by autotransfusion.

Blood Pressure Estimation Using On-body Continuous Wave Radar and Photoplethysmogram in Various Posture and Exercise Conditions

The pulse arrival time (PAT), pre-ejection period (PEP) and pulse transit time (PTT) are calculated using on-body continuous wave radar (CWR), Photoplethysmogram (PPG) and Electrocardiogram (ECG) sensors for wearable continuous systolic blood pressure (SBP) measurements. The CWR and PPG sensors are placed on the sternum and left earlobe respectively. This paper presents a signal processing method based on wavelet transform and adaptive filtering to remove noise from CWR signals. Experimental data are collected from 43 subjects in various static postures and 26 subjects doing 6 different exercise tasks. Two mathematical models are used to calculate SBPs from PTTs/PATs. For 38 subjects participating in posture tasks, the best cumulative error percentage (CEP) is 92.28% and for 21 subjects participating in exercise tasks, the best CEP is 82.61%. The results show the proposed method is promising in estimating SBP using PTT. Additionally, removing PEP from PAT leads to improving results by around 9%. The CWR sensors present a low-power, continuous and potentially wearable system with minimal body contact to monitor aortic valve mechanical activities directly. Results of this study, of wearable radar sensors, demonstrate the potential superiority of CWR-based PEP extraction for various medical monitoring applications, including BP measurement.

Inducers of post-apneic blood pressure fluctuation monitored by pulse transfer time measurement in obstructive sleep apnea varied with syndrome severity

PURPOSE

This study investigated the properties of blood pressure (BP) fluctuation and sympathovagal imbalance with the severity of OSAS.

METHODS

Nocturnal BP was continuously monitored by polysomnography for mild (n = 33), moderate (n = 34), and severe (n = 37) OSAS patients. Apnea-related systolic BP elevation (△SBP) indicated the amplitude of BP fluctuation. The SBP index, number of △SBP > 10 mmHg/h of sleep, indicated the frequency of significant BP fluctuations. The low frequency/high frequency (LF/HF) ratios indicated heart rate variability and sympathovagal imbalance.

RESULTS

△SBP and the SBP index were the highest in severe OSAS (12.9 ± 2.3 mmHg and 33.7 ± 14.7/h), followed by moderate OSAS (9.5 ± 2.6 mmHg and 7.1 ± 4.4/h), and mild OSAS (8.3 ± 1.6 mmHg and 3.4 ± 2.1/h). The LF/HF ratios in severe OSAS were significantly higher than that in moderate and mild OSAS. In mild OSAS, arousal played a more important role in BP fluctuation. In moderate OSAS, the oxygen desaturation index (ODI) and the SBP index were correlated. The difference in △SBP induced by hypoxia or by arousal was not significant. In severe OSAS, the apnea-hypopnea index (AHI) and LF/HF ratio were correlated with the SBP index, and △SBP was larger with hypoxia than arousal.

CONCLUSIONS

BP fluctuation and sympathovagal imbalance were both related to obstructive sleep apnea severity. The influence of arousal and hypoxia on BP fluctuation varied with OSAS severity.

TRIAL REGISTRATION

NCT02876471.

Continuous Blood Pressure Monitoring as a Basis for Ambient Assisted Living (AAL) - Review of Methodologies and Devices

Blood pressure (BP) is a bio-physiological signal that can provide very useful information regarding human's general health. High or low blood pressure or its rapid fluctuations can be associated to various diseases or conditions. Nowadays, high blood pressure is considered to be an important health risk factor and major cause of various health problems worldwide. High blood pressure may precede serious heart diseases, stroke and kidney failure. Accurate blood pressure measurement and monitoring plays fundamental role in diagnosis, prevention and treatment of these diseases. Blood pressure is usually measured in the hospitals, as a part of a standard medical routine. However, there is an increasing demand for methodologies, systems as well as accurate and unobtrusive devices that will permit continuous blood pressure measurement and monitoring for a wide variety of patients, allowing them to perform their daily activities without any disturbance. Technological advancements in the last decade have created opportunities for using various devices as a part of ambient assisted living for improving quality of life for people in their natural environment. The main goal of this paper is to provide a comprehensive review of various methodologies for continuous cuff-less blood pressure measurement, as well as to evidence recently developed devices and systems for continuous blood pressure measurement that can be used in ambient assisted living applications.

Evaluation of Cardiorespiratory Function During Cardiopulmonary Exercise Testing in Untreated Hypertensive and Healthy Subjects

Objective: This study aimed to compare differences in cardiorespiratory function between untreated hypertensive subjects (UHS) and healthy subjects (HS) during cardiopulmonary exercise testing (CPET). Additionally, it also aimed to explore the potential mechanisms of different exercise responses in cardiorespiratory function before, during and after CPET. Methods: Thirty subjects (15 UHS and 15 HS) were enrolled. Photoplethysmography (PPG), respiratory signal, and ECG were simultaneously collected while subjects were performing CPET. Fiducial points (a, b, c, d, e) were extracted from the second derivative of the PPG (SDPPG), and the ratios b/a, c/a, d/a, e/a, and (b-c-d-e)/a (named Aging Index, AGI) were calculated as markers of systolic and diastolic function. Additionally, respiratory rate was calculated and analyzed. Results:Before CPET, there were no significant differences in b/a, d/a, and AGI between two groups. However, after CPET, b/a (-0.9 ± 0.19 vs. -1.06 ± 0.19, p-value = 0.03) and AGI (-0.49 ± 0.75 vs. -1.15 ± 0.59, p-value = 0.011) of the UHS group were significantly higher than those of the HS. The d/a (-0.32 ± 0.24 vs. -0.14 ± 0.17, p-value = 0.024), and c/a (-0.33 ± 0.26 vs. -0.07 ± 0.19, p-value = 0.004) were significantly lower in UHS than those in HS. In contrast, before CPET, e/a (0.22 ± 0.11 vs. 0.32 ± 0.09, p-value = 0.007) in UHS was significantly lower than that in HS, while after CPET there was no significant difference between the two groups in this variable. In addition, during CPET, AGI (p-value = 0.003), and respiratory rate (p-value = 0.000) in UHS were significantly higher in comparison with before CPET. Conclusions: Different exercise responses showed the differences of cardiorespiratory function between UHS and HS. These differences not only can highlight the CV risk of UHS, but also can predict the appearance of arterial stiffness in UHS. Additionally, during CPET, significant differences in AGI, autonomic nervous function and respiratory activity assessed by respiratory rate were found between the two groups in comparison with before CPET.

A Meta-Analysis of Pulse Arrival Time Based Blood Pressure Estimation

The paper presents a preliminary meta-analysis of the sample correlation between pulse arrival time (PAT) and blood pressure (BP). The aim of the study was to verify sample correlation coefficient between PAT and BP using an affine model $ BP = a . PAT+b $ for systolic and diastolic blood pressure. The databases included in the search were the IEEE Xplore Digital Library, Springer Link and Google Scholar. Only papers from 2005 to 2017 wereincluded into analysis. The random-effects model was considered. The resulting sample correlation coefficient was equal to -0.82 (95 % CI; -0.89, -0.72) for systolic blood pressure and -0.64 (95% CI, -0.74 -0.51) for diastolic one. Egger's regression test showed that there was no evidence of publication bias. Obtained 95% CI intervals for sample correlation coefficients for SBP and DBP are almost separate, which may indicate different relation between PAT and BP for systolic and diastolic pressure.

Nocturnal blood pressure fluctuation and associated influential factors in severe obstructive sleep apnea patients with hypertension

PURPOSE

Obstructive sleep apnea syndrome (OSAS) can induce dramatic blood pressure (BP) fluctuations during sleep and it can be associated with hypertension. We investigated the properties and associated influential factors of BP fluctuation in severe OSAS with and without hypertension.

METHODS

Two hundred one severe OSAS subjects were divided into hypertensive and normotensive groups. BP was continuously monitored via measurement of pulse transmit time (PTT). The value of apnea-related systolic BP elevation (ΔSBP) was used to reflect the amplitude of BP fluctuation, and the SBP index (the number of ΔSBP > 10 mmHg per hour of sleep time) was used to stand for the frequency of significant BP fluctuations.

RESULTS

Compared with the normotensive group, △SBP and SBP index were higher in the hypertensive group (13.8 ± 4.4 mmHg vs 10.9 ± 3.1 mmHg; 44.8 ± 21.3 events/h vs 26.8 ± 15.8 events/h, all p < 0.001). Multiple regression analysis showed that percentage of sleep time with oxygen saturation < 90% (TST90) and SBP index correlated more with mean level of awakeness and sleep SBP than with apnea-hypopnea index (AHI). Analysis of all apnea events demonstrated that △SBP and the frequency of BP fluctuations were more remarkable following hypoxia than following arousal; △SBP correlated more with oxygen desaturation degree (r = 0.388, p < 0.01) and minimal SpO₂ (r = 0.392, p < 0.01) than with apnea length and desaturation duration.

CONCLUSIONS

In severe OSAS, nocturnal and awake BP levels are associated more with the nocturnal hypoxic duration and BP fluctuation than with AHI. Nocturnal BP fluctuation can be induced by both hypoxia and arousal, and especially by hypoxia.

TRIAL REGISTRATION

NCT02876471.

Ballistocardiogram-Based Approach to Cuffless Blood Pressure Monitoring: Proof of Concept and Potential Challenges

OBJECTIVE

The goal was to propose and establish the proof of concept of an ultraconvenient cuffless blood pressure monitoring approach based on the ballistocardiogram.

METHODS

The proposed approach monitors blood pressure by exploiting two features in the whole-body head-to-foot ballistocardiogram measured using a force plate: the time interval between the first ("I") and second ("J") major waves ("I-J interval") for diastolic pressure and the amplitude between the J and third major ("K") waves ("J-K amplitude") for pulse pressure. The efficacy of the approach was examined in 22 young healthy volunteers by investigating the diastolic pressure monitoring performance of pulse transit time, pulse arrival time, and ballistocardiogram's I-J interval, and the systolic pressure monitoring performance of pulse transit time and I-J interval in conjunction with ballistocardiogram's J-K amplitude.

RESULTS

The I-J interval was comparable to pulse transit time and pulse arrival time in monitoring diastolic pressure, and the J-K amplitude could provide meaningful improvement to pulse transit time and I-J interval in monitoring systolic pressure.

CONCLUSION

The ballistocardiogram may contribute toward ultraconvenient and more accurate cuffless blood pressure monitoring.

SIGNIFICANCE

The proposed approach has potential to complement the pulse transit time technique for cuffless blood pressure monitoring in two ways. First, it may be integrated with pulse transit time to enable independent monitoring of diastolic and systolic pressures via the J-K amplitude. Second, it may even enable diastolic and systolic pressure monitoring from the ballistocardiogram alone.

Development and Validation of a Novel Cuff-Less Blood Pressure Monitoring Device

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As a joint project between industry and academia, we are developing a CLB that enables BP measurement continuously and noninvasively by capturing photoplethysmographical biosignals.

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To validate the estimation of BP using a CLB in accordance with the latest wearable device standard issued by the Institute of Electrical and Electronics Engineers (IEEE 1708-2014).

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We found that CLB is technically comparable to the ordinary cuff-based BP-measuring device.

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CLB will apply for wearable health care monitoring device that may change landscape of BP measurements in terms of continuous and stress-free monitoring.

Ordinary cuff-based blood pressure–monitoring devices remain a technical limitation that disturbs activities of daily life. Here we report a novel system for the cuff-less blood pressure estimation (CLB) that requires only 1 sensor for photoplethysmography. The present study is the first report to validate and assess the clinical application of the CLB in accordance with the latest wearable device standard (issued by the Institute of Electrical and Electronics Engineers, standard 1708-2014). Our CLB is expected to offer a flexible and wearable device that permits blood pressure monitoring in more continuous and stress-free settings.

Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice

Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time (PTT) represents a well-known potential approach for ubiquitous BP monitoring. The goal of this review is to facilitate the achievement of reliable ubiquitous BP monitoring via PTT. We explain the conventional BP measurement methods and their limitations; present models to summarize the theory of the PTT-BP relationship; outline the approach while pinpointing the key challenges; overview the previous work toward putting the theory to practice; make suggestions for best practice and future research; and discuss realistic expectations for the approach.

Blood pressure monitoring during exercise: comparison of pulse transit time and volume clamp methods

During physical exercise, pulse transit time (PTT), expressed as the interval between ventricular electrical activity and peripheral pulse wave, may provide a surrogate estimate for blood pressure by the use of specific calibration procedures. The objective of this study was to determine systolic blood pressure (SBP) values derived from the PTT method and from an established method of non-invasive continuous blood pressure measurement based on the volume clamp technique, and to compare their agreement with sphygmomanometry during exercise tests. In 18 subjects, electrocardiogram (ECG) and finger-photoplethysmography were continuously recorded during maximal cycle exercise tests. Intermittent and continuous blood pressure measurements were simultaneously taken using automated sphygmomanometry and a Portapres Model-2 device, respectively. PTT was calculated for each ECG R-wave and the corresponding steepest upstroke slope in the photoplethysmogram, and was transformed to a continuous blood pressure estimate using multipoint nonlinear regression calibration based on the individual subject's sphygmomanometer readings. Bland-Altman limits of agreement between PTT-derived SBP estimates and sphygmomanometer values were -24.7 to 24.1 mmHg, and between Portapres and sphygmomanometer SBP values were -42.0 to 70.1 mmHg. For beat-to-beat SBP estimation during exercise, PTT measurement combined with multipoint nonlinear regression calibration based on intermittent sphygmomanometry may be an alternative to volume clamp devices.