Conventional pulse transit times as markers of blood pressure changes in humans

Cardiovascular and Vector-Cardiographic Effects of Articaine Anesthesia with Epinephrine

The aim was to investigate the vector-cardiographic effects in patients submitted to dental extraction under local anesthesia. Twenty-one patients aged 36.6 ± 12.4 years with a clinical and radiographic indication of mandibular or maxillary tooth extraction were enrolled. The intervention was a local or mandibular nerve block anesthesia with 4% articaine hydrochloride containing epinephrine (1 : 100,000; 40 mg/ml + 10 μg/ml). Blood pressure (BP), heart rate (HR), pulse wave transit time, and vector-cardiography data were recorded throughout 3 min before and 5 min after injection. QRS- and T-wave area under the curve (QRS AUC/T AUC) were calculated from the X/Y/Z QRS-vector or T-vector. T-wave amplitude (T AM), T AUC values, and diastolic BP decreased, and HR significantly increased 4 min after injection. A transient moderate HR drop and a corresponding small increase in T AM and T AUC immediately after the injection procedure may be explained by a decreased sympathetic tone due to psychological relief. In dental anesthesia, the systemic epinephrine effects are represented by a decrease in T AUC. These effects are most pronounced in the X- and Y-leads. The 3D determination of vector planes or amplitudes is a simple method to register the sympathetic tone in local anesthesia independently of possible effects on T-wave characteristics in single leads. In conclusion, T-wave determination may help to detect even small increases in systemic adrenaline concentration in case of accidental intravascular injection. At the same time, full rhythm and spatial ischemia control is provided.

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject's pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

Non-invasive pulse arrival time as a surrogate for oscillometric systolic blood pressure changes during non-pharmacological intervention

Background.Non-invasive continuous blood pressure (BP) monitoring is of longstanding interest in various cardiovascular scenarios. In this context, pulse arrival time (PAT), i.e., a surrogate parameter for systolic BP (change), became very popular recently, especially in the context of cuffless BP measurement and dedicated lifestyle interventions. Nevertheless, there is also understandable doubt on its reliability in uncontrolled and mobile settings.Objective.The aim of this work is therefore the investigation whether PAT follows oscillometric systolic BP readings during moderate interventions by physical or mental activity using a medical grade handheld device for non-invasive PAT assessment.Approach.A study was conducted featuring an experimental group performing a physical and a mental task, and a control group. Oscillometric BP and PAT were assessed at baseline and after each intervention. Interventions were selected randomly but then performed sequentially in a counterbalanced order. Multivariate analyses of variance were used to test within-subject and between-subject effects for the dependent variables, followed by univariate analyses for post-hoc testing. Furthermore, correlation analysis was performed to assess the association of intervention effects between BP and PAT.Mainresults.The study included 51 subjects (31 females). Multivariate analysis of variances showed that effects in BP, heart rate, PAT and pulse wave parameters were consistent and significantly different between experimental and control groups. After physical activity, heart rate and systolic BP increased significantly whereas PAT decreased significantly. Mental activity leads to a decrease in systolic BP at stable heart rate. Pulse wave parameters follow accordingly by an increase of PAT and mainly unchanged pulse wave analysis features due to constant heart rate. Finally, also the control group behaviour was accurately registered by the PAT method compared to oscillometric cuff. Correlation analyses revealed significant negative associations between changes of systolic BP and changes of PAT from baseline to the physical task (-0.33 [-0.63, 0.01],p< 0.048), and from physical to mental task (-0.51 [-0.77, -0.14],p= 0.001), but not for baseline to mental task (-0.12 [-0,43,0,20],p= 0.50) in the experimental group.Significance.PAT and the used digital, handheld device proved to register changes in BP and heart rate reliably compared to oscillometric measurements during intervention. Therefore, it might add benefit to future mobile health solutions to support BP management by tracking relative, not absolute, BP changes during non-pharmacological interventions.

Early Physiologic Numerical and Waveform Characteristics of Simulated Hemorrhagic Events With Healthy Volunteers Donating Blood

OBJECTIVES

Early signs of bleeding are often masked by the physiologic compensatory responses delaying its identification. We sought to describe early physiologic signatures of bleeding during the blood donation process.

SETTING

Waveform-level vital sign data including electrocardiography, photoplethysmography (PPG), continuous noninvasive arterial pressure, and respiratory waveforms were collected before, during, and after bleeding.

SUBJECTS

Fifty-five healthy volunteers visited blood donation center to donate whole blood.

INTERVENTION

After obtaining the informed consent, 3 minutes of resting time was given to each subject. Then 3 minutes of orthostasis was done, followed by another 3 minutes of resting before the blood donation. After the completion of donating blood, another 3 minutes of postbleeding resting time, followed by 3 minutes of orthostasis period again.

MEASUREMENTS AND MAIN RESULTS

From 55 subjects, waveform signals as well as numerical vital signs (heart rate [HR], respiratory rate, blood pressure) and clinical characteristics were collected, and data from 51 subjects were analyzable. Any adverse events (AEs; dizziness, lightheadedness, nausea) were documented. Statistical and physiologic features including HR variability (HRV) metrics and other waveform morphologic parameters were modeled. Feature trends for all participants across the study protocol were analyzed. No significant changes in HR, blood pressure, or estimated cardiac output were seen during bleeding. Both orthostatic challenges and bleeding significantly decreased time domain and high-frequency domain HRV, and PPG amplitude, whereas increasing PPG amplitude variation. During bleeding, time-domain HRV feature trends were most sensitive to the first 100 mL of blood loss, and incremental changes of different HRV parameters (from 300 mL of blood loss), as well as a PPG morphologic feature (from 400 mL of blood loss), were shown with statistical significance. The AE group (n = 6) showed decreased sample entropy compared with the non-AE group during postbleed orthostatic challenge (p = 0.003). No significant other trend differences were observed during bleeding between AE and non-AE groups.

CONCLUSIONS

Various HRV-related features were changed during rapid bleeding seen within the first minute. Subjects with AE during postbleeding orthostasis showed decreased sample entropy. These findings could be leveraged toward earlier identification of donors at risk for AE, and more broadly building a data-driven hemorrhage model for the early treatment of critical bleeding.

Ambient health sensing on passive surfaces using metamaterials

Ambient sensors can continuously and unobtrusively monitor a person's health and well-being in everyday settings. Among various sensing modalities, wireless radio-frequency sensors offer exceptional sensitivity, immunity to lighting conditions, and privacy advantages. However, existing wireless sensors are susceptible to environmental interference and unable to capture detailed information from multiple body sites. Here, we present a technique to transform passive surfaces in the environment into highly sensitive and localized health sensors using metamaterials. Leveraging textiles' ubiquity, we engineer metamaterial textiles that mediate near-field interactions between wireless signals and the body for contactless and interference-free sensing. We demonstrate that passive surfaces functionalized by these metamaterials can provide hours-long cardiopulmonary monitoring with accuracy comparable to gold standards. We also show the potential of distributed sensors and machine learning for continuous blood pressure monitoring. Our approach enables passive environmental surfaces to be harnessed for ambient sensing and digital health applications.

Combining Cardiovascular and Pupil Features Using k-Nearest Neighbor Classifiers to Assess Task Demand, Social Context, and Sentence Accuracy During Listening

In daily life, both acoustic factors and social context can affect listening effort investment. In laboratory settings, information about listening effort has been deduced from pupil and cardiovascular responses independently. The extent to which these measures can jointly predict listening-related factors is unknown. Here we combined pupil and cardiovascular features to predict acoustic and contextual aspects of speech perception. Data were collected from 29 adults (mean  =  64.6 years, SD  =  9.2) with hearing loss. Participants performed a speech perception task at two individualized signal-to-noise ratios (corresponding to 50% and 80% of sentences correct) and in two social contexts (the presence and absence of two observers). Seven features were extracted per trial: baseline pupil size, peak pupil dilation, mean pupil dilation, interbeat interval, blood volume pulse amplitude, pre-ejection period and pulse arrival time. These features were used to train k-nearest neighbor classifiers to predict task demand, social context and sentence accuracy. The k-fold cross validation on the group-level data revealed above-chance classification accuracies: task demand, 64.4%; social context, 78.3%; and sentence accuracy, 55.1%. However, classification accuracies diminished when the classifiers were trained and tested on data from different participants. Individually trained classifiers (one per participant) performed better than group-level classifiers: 71.7% (SD  =  10.2) for task demand, 88.0% (SD  =  7.5) for social context, and 60.0% (SD  =  13.1) for sentence accuracy. We demonstrated that classifiers trained on group-level physiological data to predict aspects of speech perception generalized poorly to novel participants. Individually calibrated classifiers hold more promise for future applications.

AI-enabled epidermal electronic system to automatically monitor a prognostic parameter for hypertension with a smartphone

We present a wearable, flexible, wireless and smartphone-enabled epidermal electronic system (EES) for the continuous monitoring of a prognostic parameter for hypertension. The thin and lightweight EES can be tightly attached to the chest of a patient and synchronously monitor first lead electrocardiograms (ECG) and seismocardiograms (SCG). To demonstrate the concept, we developed the EES using state-of-the-art cleanroom technologies. Two types of sensors were integrated: A pair of metal electrodes to contact the skin and to record ECG and a vibration sensor based on a thin piezoelectric polymer to record SCG from the same location of the chest, simultaneously. The complete EES was powered by the near field communication functionality of the smartphone. We developed a machine-learning algorithm and trained it on public ECG data and recorded SCG signals to extract characteristic features of the recordings. Binary classifiers were used to automatically annotate peaks. After training, the algorithm was transferred to the smartphone to continuously analyze the timing between particular ECG and SCG peaks and to extract the Weissler's index as a prognostic parameter for hypertension. Tests with data of healthy control persons and clinical experiments with patients diagnosed with cardio-pulmonary hypertension showed a promising prognostic performance. The presented EES technology could be utilized for pre-screening of cardio-pulmonary hypertension, which is a strong burden in our today's healthcare system.

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.

A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring

The complex relationships between continuously monitored health signals and therapeutic regimens can be modelled via machine learning. However, the clinical implementation of the models will require changes to clinical workflows. Here we outline ClinAIOps ('clinical artificial-intelligence operations'), a framework that integrates continuous therapeutic monitoring and the development of artificial intelligence (AI) for clinical care. ClinAIOps leverages three feedback loops to enable the patient to make treatment adjustments using AI outputs, the clinician to oversee patient progress with AI assistance, and the AI developer to receive continuous feedback from both the patient and the clinician. We lay out the central challenges and opportunities in the deployment of ClinAIOps by means of examples of its application in the management of blood pressure, diabetes and Parkinson's disease. By enabling more frequent and accurate measurements of a patient's health and more timely adjustments to their treatment, ClinAIOps may substantially improve patient outcomes.

Non-Invasive Blood Pressure Sensing via Machine Learning

In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard.

A Wearable System for Continuous Monitoring and Assessment of Speech, Gait, and Cognitive Decline for Early Diagnosis of ADRD

Early detection of cognitive decline is essential to study mild cognitive impairment and Alzheimer's Disease in order to develop targeted interventions and prevent or stop the progression of dementia. This requires continuous and longitudinal assessment and tracking of the related physiological and behavioral changes during daily life. In this paper, we present a low cost and low power wearable system custom designed to track the trends in speech, gait, and cognitive stress while also considering the important human factor needs such as privacy and compliance. In the form factors of a wristband and waist-patch, this multimodal, multi-sensor system measures inertial signals, sound, heart rate, electrodermal activity and pulse transit time. A total power consumption of 2.6 mW without any duty cycling allows for more than 3 weeks of run time between charges when 1500 mAh batteries are used.Clinical Relevance- Much earlier detection of Alzheimer's disease and related dementias may be possible by continuous monitoring of physiological and behavioral state using application specific wearable sensors during the activities of daily life.

A study on the immediate effects of enhanced external counterpulsation on physiological coupling

Introduction

Enhanced external counterpulsation (EECP) is a non-invasive assisted circulation technique for its clinical application in the rehabilitation and management of ischemic cardiovascular and cerebrovascular diseases, which has complex physiological and hemodynamic effects. However, the effects of EECP on the coupling of physiological systems are still unclear. We aimed to investigate the immediate effects of EECP on the coupling between integrated physiological systems such as cardiorespiratory and cardiovascular systems.

Methods

Based on a random sham-controlled design, simultaneous electrocardiography, photoplethysmography, bio-electrical impedance, and continuous hemodynamic data were recorded before, during and after two consecutive 30 min EECP in 41 healthy adults. Physiological coupling strength quantified by phase synchronization indexes (PSI), hemodynamic measurements and heart rate variability indices of 22 subjects (female/male: 10/12; age: 22.6 ± 2.1 years) receiving active EECP were calculated and compared with those of 19 sham control subjects (female/male: 7/12; age: 23.6 ± 2.5 years).

Results

Immediately after the two consecutive EECP interventions, the physiological coupling between respiratory and cardiovascular systems PSI_RES-PTT (0.34 ± 0.14 vs. 0.49 ± 0.17, P = 0.002), the physiological coupling between cardiac and cardiovascular systems PSI_IBI-PTT (0.41 ± 0.14 vs. 0.52 ± 0.16, P = 0.006) and the total physiological coupling PSI_total (1.21 ± 0.35 vs. 1.57 ± 0.49, P = 0.005) in the EECP group were significantly lower than those before the EECP intervention, while the physiological coupling indexes in the control group did not change significantly (P > 0.05).

Conclusion

Our study provides evidence that the PSI is altered by immediate EECP intervention. We speculate that the reduced PSI induced by EECP may be a marker of disturbed physiological coupling. This study provides a new method for exploring the mechanism of EECP action and may help to further optimize the EECP technique.

Simultaneous photoplethysmography and blood flow measurements towards the estimation of blood pressure using speckle contrast optical spectroscopy

Non-invasive continuous blood pressure monitoring remains elusive. There has been extensive research using the photoplethysmographic (PPG) waveform for blood pressure estimation, but improvements in accuracy are still needed before clinical use. Here we explored the use of an emerging technique, speckle contrast optical spectroscopy (SCOS), for blood pressure estimation. SCOS provides measurements of both blood volume changes (PPG) and blood flow index (BFi) changes during the cardiac cycle, and thus provides a richer set of parameters compared to traditional PPG. SCOS measurements were taken on the finger and wrists of 13 subjects. We investigated the correlations between features extracted from both the PPG and BFi waveforms with blood pressure. Features from the BFi waveforms were more significantly correlated with blood pressure than PPG features ( R = - 0.55, p = 1.1 × 10^-4 for the top BFi feature versus R = - 0.53, p = 8.4 × 10^-4 for the top PPG feature). Importantly, we also found that features combining BFi and PPG data were highly correlated with changes in blood pressure ( R = - 0.59, p  = 1.7 × 10^-4 ). These results suggest that the incorporation of BFi measurements should be further explored as a means to improve blood pressure estimation using non-invasive optical techniques.

Photoplethysmography Signal Wavelet Enhancement and Novel Features Selection for Non-Invasive Cuff-Less Blood Pressure Monitoring

In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from 1080 different patients. Three feature selection methods, namely Correlation-based Feature Selection (CFS), RReliefF and Minimum Redundancy Maximum Relevance (MRMR), have then been applied to identify the most significant features for BP estimation. Some of these features have been extracted through a novel PPG signal enhancement method based on the use of the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the enhanced signal leads to a reliable identification of the characteristic points of the PPG signal (e.g., systolic, diastolic and dicrotic notch points) by simple means, obtaining results comparable with those from purposely defined algorithms. For systolic points, mean and std of errors computed as the difference between the locations obtained using a purposely defined already known algorithm and those using the MODWT enhancement are, respectively, 0.0097 s and 0.0202 s; for diastolic points they are, respectively, 0.0441 s and 0.0486 s; for dicrotic notch points they are 0.0458 s and 0.0896 s. Hence, this study leads to the selection of several new features from the MODWT enhanced signal on every single pulse extracted from PPG signals, in addition to features already known in the literature. These features can be employed to train machine learning (ML) models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a non-invasive way, which is suitable for telemedicine health-care monitoring.

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.

Applied machine learning for blood pressure estimation using a small, real-world electrocardiogram and photoplethysmogram dataset

Applying machine learning techniques to electrocardiography and photoplethysmography signals and their multivariate-derived waveforms is an ongoing effort to estimate non-occlusive blood pressure. Unfortunately, real ambulatory electrocardiography and photoplethysmography waveforms are inevitably affected by motion and noise artifacts, so established machine learning architectures perform poorly when trained on data of the Multiparameter Intelligent Monitoring in Intensive Care II type, a publicly available ICU database. Our study addresses this problem by applying four well-established machine learning methods, i.e., random forest regression, support vector regression, Adaboost regression and artificial neural networks, to a small, self-sampled electrocardiography-photoplethysmography dataset (n = 54) to improve the robustness of machine learning to real-world BP estimates. We evaluated the performance using a selection of optimal feature morphologies of waveforms by using pulse arrival time, morphological and frequency photoplethysmography parameters and heart rate variability as characterization data. On the basis of the root mean square error and mean absolute error, our study showed that support vector regression gave the best performance for blood pressure estimation from noisy data, achieving an mean absolute error of 6.97 mmHg, which meets the level C criteria set by the British Hypertension Society. We demonstrate that ambulatory electrocardiography- photoplethysmography signals acquired by mobile discrete devices can be used to estimate blood pressure.

Long-term stability of over-the-counter cuffless blood pressure monitors: a proposal

Blood pressure is an important cardiovascular parameter. Currently, the cuff-based sphygmomanometer is a popular, reliable, measurement method, but blood pressure monitors without cuffs have become popular and are now available without a prescription. Blood pressure monitors must be approved by regulatory authorities. Current cuffless blood pressure (CL-BP) monitors are not suitable for at-home management and prevention of hypertension. This paper proposes simple criteria for over-the-counter CL-BP monitoring. First, the history of the sphygmomanometer and current standard blood pressure protocol are reviewed. The main components of CL-BP monitoring are accuracy during the resting condition, accuracy during dynamic blood pressure changes, and long-term stability. In this proposal we recommend intermittent measurement to ensure that active measurement accuracy mirrors resting condition accuracy. A new experimental protocol is proposed to maintain long-term stability. A medically approved automated sphygmomanometer was used as the standard device in this study. The long-term accuracy of the test device is based on the definition of propagation error, i.e., for an oscillometric automated sphygmomanometer (5 ± 8 mmHg) ± the error for the test device static accuracy (-0.12 ± 5.49 mmHg for systolic blood pressure and - 1.17 ± 5.06 mmHg for diastolic blood pressure). Thus, the long-term stabilities were - 3.38 ± 7.1 mmHg and - 1.38 ± 5.4 mmHg, which satisfied propagation error. Further research and discussion are necessary to create standards for use by manufacturers; such standards should be readily evaluated and ensure high-quality evidence.

Supplementary information

The online version contains supplementary material available at 10.1007/s12553-023-00726-6.

Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals

Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients' health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.

Subject-wise model generalization through pooling and patching for regression: Application on non-invasive systolic blood pressure estimation

BACKGROUND

Subject-wise modeling using machine learning is useful in many applications requiring low error and complexity, such as wearable medical devices. However, regression accuracy depends highly on the data available to train the model and the model's generalization ability. Adversely, the prediction error may increase severely if unknown data patterns test the model; such a model is known to be overfitted. In medicine-related applications, such as Non-Invasive Blood Pressure (NIBP) estimation, the high error renders the estimation model useless and dangerous.

METHODS

This paper presents a novel algorithm to handle overfitting by editing the training data to achieve generalization for subject-wise models. The pooling and patching (PaP) algorithms use a relatively short record segment of a subject as a Key-Segment (KS) to search through a larger dataset for similar subjects. Then samples taken from the matched subjects' pool records are used to patch the original subject's KS. Due to the significance of systolic blood pressure (SBP) and the complexity of its variability, non-invasive estimation of SBP from electrocardiography (ECG) and photoplethysmography (PPG) is introduced as an application to assess the algorithm. The study was performed on 2051 subjects with a wide range of age, height, weight, length, and health status. The subjects' records were taken from a large public dataset, VitalDB, which is acquired from subjects undergoing different surgeries. Finally, all the results are obtained without using other model generalization techniques.

RESULTS

The generalization effect of the proposed algorithm, PaP, significantly outperformed cross-validation, which is widely used in regression model generalization. Moreover, the testing results show that a KS of 200 to 2000 samples is sufficient for providing high accuracy for much longer testing data of about 12000 to 24000 samples long, which is less than %10 of the record length on average. Furthermore, compared to other works based on the same dataset, PaP provides a significantly lower mean error of -0.75 ± 5.51 mmHg, with a small training data portion of 15% over 2051 subjects.

Cuffless blood pressure estimation using chaotic features of photoplethysmograms and parallel convolutional neural network

BACKGROUND AND OBJECTIVE

As a nonlinear framework in dynamical system analysis, chaotic approaches are mainly applied to evolve the complexity of biological systems. Due to the chaotic nature of the cardiovascular systems, the nonlinear features can intuitively provide a reliable framework in blood pressure (BP) estimation. Cuffless BP estimation is usually carried out by establishing deep neural network models estimating the BP values through machine-learned features of photoplethysmogram (PPG) signals.

METHODS

In this study, a novel parallel deep architecture is proposed to handle the machine-learned and chaotic features of PPG signals in estimating the actual BP values. The chaotic handcrafted features were the signal properties associated with the Poincare sections in the phase space and the recurrence plot-based measures called recurrence quantification analysis (RQA). Moreover, the measures quantifying the nonlinear properties of the temporal sequences such as correlation dimension, fractal dimension, Lyapunov exponent, and entropy-based quantities were also employed. The parallel architecture not only embedded the chaotic nature of PPG signals but also provided a facility to include the pseudo-periodic variations of PPGs by utilizing a concatenating layer.

RESULTS

Our framework was examined on the public dataset, namely, Multi-Parameter Intelligent in Intensive Care II contained the recording of PPG, ECG and arterial blood pressure. The performance of the employed handcrafted features in distinguishing between the levels of BP values was investigated based on Spearman's statistics. In addition, our proposed scheme is evaluated in terms of Pearson's correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). The best performance was achieved when the employed handcrafted features accompanied by PPG sequences were applied to the parallel deep network. In particular, the values of R, RMSE, and MAE were obtained 0.9529, 2.76 mmHg, and 1.73 mmHg for diastolic BP, and 0.9444, 6.18 mmHg, and 3.8 mmHg for systolic BP, respectively. Moreover, based on the requirements of the standards set by the British Hypertension Society (BHS), the proposed scheme achieved a grade of A.

CONCLUSIONS

Our proposed scheme outperformed the state-of-the-art BP estimation methods. In addition, the results confirmed that the concatenation of the PPG-related machine-learned and nonlinear handcrafted features can be properly applied in continuous BP monitoring.

Intra-beat biomarker for accurate continuous non-invasive blood pressure monitoring

Accurate continuous non-invasive blood pressure (CNIBP) monitoring is the holy grail of digital medicine but remains elusive largely due to significant drifts in signal and motion artifacts that necessitate frequent device recalibration. To address these challenges, we developed a unique approach by creating a novel intra-beat biomarker (Diastolic Transit Time, DTT) to achieve highly accurate blood pressure (BP) estimations. We demonstrated our approach’s superior performance, compared to other common signal processing techniques, in eliminating stochastic baseline wander, while maintaining signal integrity and measurement accuracy, even during significant hemodynamic changes. We applied this new algorithm to BP data collected using non-invasive sensors from a diverse cohort of high acuity patients and demonstrated that we could achieve close agreement with the gold standard invasive arterial line BP measurements, for up to 20 min without recalibration. We established our approach's generalizability by successfully applying it to pulse waveforms obtained from various sensors, including photoplethysmography and capacitive-based pressure sensors. Our algorithm also maintained signal integrity, enabling reliable assessments of BP variability. Moreover, our algorithm demonstrated tolerance to both low- and high-frequency motion artifacts during abrupt hand movements and prolonged periods of walking. Thus, our approach shows promise in constituting a necessary advance and can be applied to a wide range of wearable sensors for CNIBP monitoring in the ambulatory and inpatient settings.

Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach

Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed.

The Effects of Filtering PPG Signal on Pulse Arrival Time-Systolic Blood Pressure Correlation

Pulse arrival time (PAT), evaluated from electro-cardiogram (ECG) and photoplethysmogram (PPG) signals, has been widely used for cuff-less blood pressure (BP) estimation due to its high correlation with BP. However, the question of whether filtering the PPG signal impacts the extracted PAT values and consequently, the correlation between PAT and BP, has not been investigated before. In this paper, using data from 18 subjects, changes in the PAT values, and in the subject-specific PAT-systolic BP (SBP) correlation caused by filtering the PPG signal with variable cutoff frequencies in the range of 2 to 15 Hz are studied. For PAT extraction, three PPG characteristic points (foot, maximum slope and systolic peak) are considered. Results show that differences in the cutoff frequency can shift the PAT values and introduce a worst-case error of over 8.2 mmHg for SBP estimation, indicating that PPG signal filter settings can impact PAT-based BP estimations. Our study suggests that extracting the PAT from the maximum slope point of PPG signal filtered at 10 Hz provides the most stable correlation with SBP.

[State-certified Health Professionals Supported by Telemedicine in the On-call Service of Community Doctors]

Background

Regional Associations of Statutory Health Insurance Physicians in Germany are obliged to provide around-the-clock non-emergency care. This includes out-of-hours care, weekends, and public holidays. Due to a growing shortage of specialised doctors in private practice - especially in more rural area - this well-established system might be about to collapse. A possible solution could lie in training medical personnel to work under telemedical supervision.

Methods

The Association of Statutory Health Insurance Physicians of Lower Saxony, Klinikum Oldenburg AöR, and Johanniter-Unfall-Hilfe (JUH) Germany developed a pilot scheme in which specially-trained paramedics provided out-of-hours non-emergency medical services. Supervision was ensured by telemedical backup. Descriptive statistics were used to analyse the data.

Results

The majority of cases occurred in urban areas in the afternoon or at night. Most problems could be resolved by providing patient counselling. The majority of patients presented with unspecific symptoms. Telemedical support by a medical doctor was only required in 2.1 % of cases. In Germany, treatment by paramedics is widely accepted. In 63.5 % of cases patients could remain in an ambulatory setting.

Conclusions

Our study showed that non-emergency out-of-hours service can be competently delivered by paramedics under telemedical supervision. However, the expansion of high-speed wireless internet access is crucial for ensuring nationwide telemedical support. More research is needed to find out whether the described pilot scheme can be rolled out or even expanded in more rural areas of Germany.

A fusion approach to improve accuracy and estimate uncertainty in cuffless blood pressure monitoring

A substantial barrier to the clinical adoption of cuffless blood pressure (BP) monitoring techniques is the lack of unified error standards and methods of estimating measurement uncertainty. This study proposes a fusion approach to improve accuracy and estimate prediction interval (PI) as a proxy for uncertainty for cuffless blood BP monitoring. BP was estimated during activities of daily living using three model architectures: nonlinear autoregressive models with exogenous inputs, feedforward neural network models, and pulse arrival time models. Multiple one-class support vector machine (OCSVM) models were trained to cluster data in terms of the percentage of outliers. New BP estimates were then assigned to a cluster using the OCSVMs hyperplanes, and the PIs were estimated using the BP error standard deviation associated with different clusters. The OCSVM was used to estimate the PI for the three BP models. The three BP estimations from the models were fused using the covariance intersection fusion algorithm, which improved BP and PI estimates in comparison with individual model precision by up to 24%. The employed model fusion shows promise in estimating BP and PI for potential clinical uses. The PI indicates that about 71%, 64%, and 29% of the data collected from sitting, standing, and walking can result in high-quality BP estimates. Our PI estimator offers an effective uncertainty metric to quantify the quality of BP estimates and can minimize the risk of false diagnosis.

Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings

Epilepsy is a chronic disease with a significant social impact, given that the patients and their families often live conditioned by the possibility of an epileptic seizure and its possible consequences, such as accidents, injuries, or even sudden unexplained death. In this context, ambulatory monitoring allows the collection of biomedical data about the patients’ health, thus gaining more knowledge about the physiological state and daily activities of each patient in a more personalized manner. For this reason, this article proposes a novel monitoring system composed of different sensors capable of synchronously recording electrocardiogram (ECG), photoplethysmogram (PPG), and ear electroencephalogram (EEG) signals and storing them for further processing and analysis in a microSD card. This system can be used in a static and/or ambulatory way, providing information about the health state through features extracted from the ear EEG signal and the calculation of the heart rate variability (HRV) and pulse travel time (PTT). The different applied processing techniques to improve the quality of these signals are described in this work. A novel algorithm used to compute HRV and PTT robustly and accurately in ambulatory settings is also described. The developed device has also been validated and compared with other commercial systems obtaining similar results. In this way, based on the quality of the obtained signals and the low variability of the computed parameters, even in ambulatory conditions, the developed device can potentially serve as a support tool for clinical decision-taking stages.

Cuffless Blood Pressure Measurement

Cuffless blood pressure (BP) measurement has become a popular field due to clinical need and technological opportunity. However, no method has been broadly accepted hitherto. The objective of this review is to accelerate progress in the development and application of cuffless BP measurement methods. We begin by describing the principles of conventional BP measurement, outstanding hypertension/hypotension problems that could be addressed with cuffless methods, and recent technological advances, including smartphone proliferation and wearable sensing, that are driving the field. We then present all major cuffless methods under investigation, including their current evidence. Our presentation includes calibrated methods (i.e., pulse transit time, pulse wave analysis, and facial video processing) and uncalibrated methods (i.e., cuffless oscillometry, ultrasound, and volume control). The calibrated methods can offer convenience advantages, whereas the uncalibrated methods do not require periodic cuff device usage or demographic inputs. We conclude by summarizing the field and highlighting potentially useful future research directions. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 24 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.

A Wearable All-Gel Multimodal Cutaneous Sensor Enabling Simultaneous Single-Site Monitoring of Cardiac-Related Biophysical Signals

The human cutaneous sensory organ is a highly evolved biosensor that is efficient, sensitive, selective, and adaptable. Recently, with the development of various materials and structures inspired by sensory organs, artificial cutaneous sensors have been widely studied. In this study, the acquisition of biophysical signals is demonstrated at one point on the body using a wearable all-gel-integrated multimodal sensor composed of four element sensors, inspired by the slow/rapid adapting functions of the skin sensory receptors. The gel-type sensors ensure flexibility, compactness, portability, adherence, and integrity. The wearable all-gel multimodal sensor is easily attached to the wrist and simultaneously gathers blood pressure (BP), electrocardiogram (ECG), electromyogram (EMG), and mechanomyogram (MMG) signals related to cardiac and muscle health. Human activity causes muscle contraction, which affects blood flow; therefore, the relationship between the muscle and heart is crucial for screening and predicting heart health. Cardiac health is monitored by obtaining the two types of phase time differences (i.e., Δt_be : BP and ECG, Δt_em : ECG and MMG) generated during muscle movement. The suggested multimodal sensor has potential applicability in monitoring biophysical conditions and diagnosing cardiac-related health problems.

External factors that affect the photoplethysmography waveforms

Photoplethysmography (PPG) is a simple and inexpensive technology used in many smart devices to monitor cardiovascular health. The PPG sensors use LED lights to penetrate into the bloodstream to detect the different blood volume changes in the tissue through skin contact by sensing the amount of light that hits the sensor. Typically, the data are displayed on a graph and it forms the pulse waveform. The information from the produced pulse waveform can be useful in calculating measurements that help monitor cardiovascular health, such as blood pressure. With many more people beginning to monitor their health status on their smart devices, it is extremely important that the PPG signal is accurate. Designing a simple experiment with standard laboratory equipment and commercial sensors, we wanted to find how external factors influence the results. In this study, it was found that external factors, touch force and temperature, can have a large impact on the resulting waveform, so the effects of those factors need to be considered in order for the information to become more reliable.

Photoplethysmography Fast Upstroke Time Intervals Can Be Useful Features for Cuff-Less Measurement of Blood Pressure Changes in Humans

OBJECTIVE

Photoplethysmography (PPG) waveform analysis is being increasingly investigated for continuous, non-invasive, and cuff-less blood pressure (BP) measurement. However, the efficacy of this approach and the useful features and models remain largely unclear. The objectives were to develop easy-to-understand models relating PPG waveform features to BP changes (after a cuff calibration) and to determine their value in BP measurement accuracy.

METHODS

The study data comprised finger, toe, and ear PPG waveforms, an ECG waveform, and reference manual cuff BP measurements from 32 human subjects (25% hypertensive) before and after slow breathing, mental arithmetic, cold pressor, and nitroglycerin administration. Stepwise linear regression was employed to create parsimonious models for predicting the intervention-induced BP changes from popular PPG waveform features, pulse arrival time (PAT, time delay between ECG R-wave and PPG foot), and subject demographics. Leave-one-subject-out cross validation was applied to compare the BP change prediction root-mean-squared-errors (RMSEs) of the resulting models to reference models in which PPG waveform features were excluded.

RESULTS

Finger b-time (PPG foot to minimum second derivative time interval) and ear "STT" (PPG amplitude divided by maximum derivative), when combined with PAT, reduced the systolic BP change prediction RMSE of reference models by 6-7% (p 0.022). Ear STT together with pulse width reduced the diastolic BP change prediction RMSE of the reference model by 13% (p = 0.003).

CONCLUSION

The two PPG fast upstroke time intervals can offer some added value in cuff-less BP trending.

SIGNIFICANCE

This study offers important information towards achieving non-invasive and passive BP monitoring without a cuff.

Assessment of Calibration Models for Cuff-Less Blood Pressure Measurement After One Year of Aging

OBJECTIVE

Many calibration models for cuff-less blood pressure (BP) measurement must be periodically updated with cuff BP values to account for vascular aging. However, the time period required for these cuff re-calibrations is largely unknown. The impact of one year of aging on several calibration models was assessed.

METHODS

Ten humans (6 males, 5718 years, 3 hypertensives) were studied during multiple recording sessions that occurred one year apart. In each session, electrocardiography (ECG), ear photoplethysmography (PPG), finger PPG, and toe PPG waveforms and manual cuff BP were recorded before and after slow breathing, mental arithmetic, cold pressor, and nitroglycerin. Linear models based on each PPG waveform, which were previously shown to offer value in predicting the intervention-induced BP changes in a larger subject cohort, were employed. The model coefficients were determined for each subject via one session, and the fully-defined, subject-specific calibration models were then evaluated in the corresponding subjects via the session one year later.

RESULTS

Only a linear model relating toe pulse arrival time (PAT) time delay between ECG R-wave and toe PPG foot to systolic BP (SBP) remained useful. After the year, this model changed little on average (root-mean-squared-error (RMSE) = 1.5 mmHg) and predicted the cuff BP values better than the average of the initial cuff BP values of the subject (RMSE = 9.60.8 mmHg vs. 12.71.0 mmHg; p < 0.05).

CONCLUSION

These results suggest annual cuff recalibrations for the toe PAT-SBP model.

SIGNIFICANCE

Toe PAT may offer a practical recalibration period that fosters user adherence.

Blood pressure wave propagation - a multisensor setup for cerebral autoregulation studies

OBJECTIVE

Cerebral autoregulation is critically important to maintain proper brain perfusion and supply the brain with oxygenated blood. Non-invasive measures of blood pressure (BP) are critical in assessing cerebral autoregulation. Wave propogation velocity may be a useful technique to estimate BP but the effect of the location of the sensors on the readings has not been thoroughly examined. In this paper, we were interested to study if propagation velocity of the pressure wave in the direction from the heart to the brain may differ compared with propagation from the heart to the periphery, as well as across different physiological tasks and/or health conditions. Using non-invasive sensors simultaneously placed on different locations of the human body allow for the study of how propagation velocity of the pressure wave, based on pulse transit time (PTT), varies across different directions.

APPROACH

We present multi-sensor BP wave propagation measurement setup aimed for cerebral autoregulation studies. The presented sensor setup consists of three sensors, one each placed on the neck, chest and finger, allowing simultaneous measurement of changes in BP propagation velocity towards the brain and to the periphery. We show how commonly tested physiological tasks affect the relative changes of PTT and correlations with BP.

MAIN RESULTS

We observed that during maximal blow, valsalva and breath hold breathing tasks, the relative changes of PTT were higher when PTT was measured in the direction from the heart to the brain than from the heart to the peripherals. In contrast, during a deep breathing task, the relative change in PTT from the heart to the brain was lower. In addition, we present a short literature review of PTT methods used in brain research.

SIGNIFICANCE

These preliminary data suggest that physiological task and direction of PTT measurement may affect relative PTT changes. Presented three-sensor setup provides an easy and neuroimaging compatible method for cerebral autoregulation studies by allowing to measure BP wave propagation velocity towards the brain vs. towards the periphery.

PulseLab: An Integrated and Expandable Toolbox for Pulse Wave Velocity-based Blood Pressure Estimation

In this paper, we introduce PulseLab, a comprehensive MATLAB toolbox that enables estimating the blood pressure (BP) from electrocardiogram (ECG) and photoplethysmogram (PPG) signals using pulse wave velocity (PWV)-based models. This universal framework consists of 6 sequential modules, covering end-to-end procedures that are needed for estimating BP from raw PPG/ECG data. These modules are "dataset formation", "signal pre-processing", "segmentation", "characteristic-points detection", "pulse transit time (PTT)/ pulse arrival time (PAT) calculation", and "model validation". The toolbox is expandable and its application programming interface (API) is built such that newly-derived PWV-BP models can be easily included. The toolbox also includes a user-friendly graphical user interface (GUI) offering visualization for step-by-step processing of physiological signals, position of characteristic points, PAT/PTT values, and the BP regression results. To the best of our knowledge, PulseLab is the first comprehensive toolbox that enables users to optimize their model by considering several factors along the process for obtaining the most accurate model for cuff-less BP estimation.

Evaluation of a dual-PPG system for pulse transit time monitoring

This work presents a new dual-photoplethysmographic (PPG) system for pulse transit time (PTT) monitoring. An experiment has been set up in order to compare the PTT measurement between carotid and radial arteries from two systems: our physiological multimodal platform (PMP) and the Complior® tonometer. This work explores the comparison between such optical and mechanical modalities. The results show that the PPG device tends to overestimate the PTT (RMSE = 16 ms). Furthermore, both mechanical and optical signals have been superposed and demonstrated that pulse morphologies are quite similar.Clinical Relevance-Carotid-radial pulse wave velocity (PWV) is compared on a small cohort of subjects and significant differences are observed between optical and mechanical-based systems.

Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics

Telemedicine has become an increasingly important part of the modern healthcare infrastructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.