Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals

Pediatric Monitoring Technologies and Congenital Heart Disease: A Systematic Review

Outpatient monitoring of infants with congenital heart disease has been shown to significantly reduce rates of mortality in the single ventricle population. Despite the accelerating development of miniaturized biosensors and electronics, and a growing market demand for at-home monitoring devices, the application of these technologies to infants and children is significantly delayed compared with the development of devices for adults. This article aims to review the current landscape of available monitoring technologies and devices for pediatric patients to describe the gap between technologies and clinical needs with the goal of progressing development of clinically and scientifically validated pediatric monitoring devices.

Heartbeat detection from high-density EMG electrodes on the upper arm at different EMG intensity levels using Zephlet

BACKGROUND AND OBJECTIVES

A significant number of global deaths caused by cardiac arrhythmias can be prevented with accurate and immediate identification. Wearable devices can play a critical role in such identification by continuously monitoring cardiac activity using electrocardiogram (ECG). The existing body of research has focused on extracting cardiac information from the body surface by investigating various electrode locations and algorithm development for ECG interpretation. The present study was designed for heartbeat detection using the signals recorded from the upper arm.

METHODS

Firstly, optimal electrode locations on the upper arm were identified for Rest and elbow flexion (EF) conditions. Next, a synthesized ECG was generated using the selected electrodes with generalized weights over subjects and trials, and then zero-phase wavelet (Zephlet) was applied for feature extraction. Heartbeat detection was finally performed using the extracted detail coefficients incorporated with a multiagent detection scheme (MDS).

RESULTS

The F1-score for heartbeat detection was 0.94  ±  0.16, 0.86  ±  0.22, 0.79  ±  0.26, and 0.67  ±  0.31 for Rest and EF with three different levels of muscle contraction (C1 to C3), respectively. Changing the acceptable distance between the detected and actual heartbeats from 50 ms to 20 ms, the F1-score changed to 0.81  ±  0.20, 0.66  ±  0.26, 0.57  ±  0.26,  and 0.44  ±  0.26 for Rest and C1 to C3, respectively.

CONCLUSION

These findings make several contributions to the current literature, summarized as precise and consistent electrode localization for various muscle contraction levels and accurate heartbeat detection method development for each of these conditions.

Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction

BACKGROUND

Internet-of-things technologies are reshaping healthcare applications. We take a special interest in long-term, out-of-clinic, electrocardiogram (ECG)-based heart health management and propose a machine learning framework to extract crucial patterns from noisy mobile ECG signals.

METHODS

A three-stage hybrid machine learning framework is proposed for estimating heart-disease-related ECG QRS duration. First, raw heartbeats are recognized from the mobile ECG using a support vector machine (SVM). Then, the QRS boundaries are located using a novel pattern recognition approach, multiview dynamic time warping (MV-DTW). To enhance robustness with motion artifacts in the signal, the MV-DTW path distance is also used to quantize heartbeat-specific distortion conditions. Finally, a regression model is trained to transform the mobile ECG QRS duration into the commonly used standard chest ECG QRS durations.

RESULTS

With the proposed framework, the performance of ECG QRS duration estimation is very encouraging, and the correlation coefficient, mean error/standard deviation, mean absolute error, and root mean absolute error are 91.2%, 0.4 ± 2.6, 1.7, and 2.6 ms, respectively, compared with the traditional chest ECG-based measurements.

CONCLUSIONS

Promising experimental results are demonstrated to indicate the effectiveness of the framework. This study will greatly advance machine-learning-enabled ECG data mining towards smart medical decision support.

Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor

The rapid evolution of wearable technology in healthcare sectors has created the opportunity for people to measure their blood pressure (BP) using a smartwatch at any time during their daily activities. Several commercially-available wearable devices have recently been equipped with a BP monitoring feature. However, concerns about recalibration remain. Pulse transit time (PTT)-based estimation is required for initial calibration, followed by periodic recalibration. Recalibration using arm-cuff BP monitors is not practical during everyday activities. In this study, we investigated recalibration using PTT-based BP monitoring aided by a deep neural network (DNN) and validated the performance achieved with more practical wrist-cuff BP monitors. The PTT-based prediction produced a mean absolute error (MAE) of 4.746 ± 1.529 mmHg for systolic blood pressure (SBP) and 3.448 ± 0.608 mmHg for diastolic blood pressure (DBP) when tested with an arm-cuff monitor employing recalibration. Recalibration clearly improved the performance of both DNN and conventional linear regression approaches. We established that the periodic recalibration performed by a wrist-worn BP monitor could be as accurate as that obtained with an arm-worn monitor, confirming the suitability of wrist-worn devices for everyday use. This is the first study to establish the potential of wrist-cuff BP monitors as a means to calibrate BP monitoring devices that can reliably substitute for arm-cuff BP monitors. With the use of wrist-cuff BP monitoring devices, continuous BP estimation, as well as frequent calibrations to ensure accurate BP monitoring, are now feasible.

Convolutional Neural Network for Individual Identification Using Phase Space Reconstruction of Electrocardiogram

Electrocardiogram (ECG) biometric provides an authentication to identify an individual on the basis of specific cardiac potential measured from a living body. Convolutional neural networks (CNN) outperform traditional ECG biometrics because convolutions can produce discernible features from ECG through machine learning. Phase space reconstruction (PSR), using a time delay technique, is one of the transformations from ECG to a feature map, without the need of exact R-peak alignment. However, the effects of time delay and grid partition on identification performance have not been investigated. In this study, we developed a PSR-based CNN for ECG biometric authentication and examined the aforementioned effects. Based on a population of 115 subjects selected from the PTB Diagnostic ECG Database, a higher identification accuracy was achieved when the time delay was set from 20 to 28 ms, since it produced a well phase-space expansion of P, QRS, and T waves. A higher accuracy was also achieved when a high-density grid partition was used, since it produced a fine-detail phase-space trajectory. The use of a scaled-down network for PSR over a low-density grid with 32 × 32 partitions achieved a comparable accuracy with using a large-scale network for PSR over 256 × 256 partitions, but it had the benefit of reductions in network size and training time by 10 and 5 folds, respectively.

Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review

Continuous blood pressure (BP) monitoring is of great significance for the real-time monitoring and early prevention of cardiovascular diseases. Recently, wearable BP monitoring devices have made great progress in the development of daily BP monitoring because they adapt to long-term and high-comfort wear requirements. However, the research and development of wearable continuous BP monitoring devices still face great challenges such as obvious motion noise and slow dynamic response speeds. The pulse wave transit time method which is combined with photoplethysmography (PPG) waves and electrocardiogram (ECG) waves for continuous BP monitoring has received wide attention due to its advantages in terms of excellent dynamic response characteristics and high accuracy. Here, we review the recent state-of-art wearable continuous BP monitoring devices and related technology based on the pulse wave transit time; their measuring principles, design methods, preparation processes, and properties are analyzed in detail. In addition, the potential development directions and challenges of wearable continuous BP monitoring devices based on the pulse wave transit time method are discussed.

Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve

Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the "black-box" nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients' risks and necessary therapy adjustments due to changes in disease progression and/or therapy response.

Wearables in Nephrology: Fanciful Gadgetry or Prêt-à-Porter?

Telemedicine and digitalised healthcare have recently seen exponential growth, led, in part, by increasing efforts to improve patient flexibility and autonomy, as well as drivers from financial austerity and concerns over climate change. Nephrology is no exception, and daily innovations are underway to provide digitalised alternatives to current models of healthcare provision. Wearable technology already exists commercially, and advances in nanotechnology and miniaturisation mean interest is also garnering clinically. Here, we outline the current existing wearable technology pertaining to the diagnosis and monitoring of patients with a spectrum of kidney disease, give an overview of wearable dialysis technology, and explore wearables that do not yet exist but would be of great interest. Finally, we discuss challenges and potential pitfalls with utilising wearable technology and the factors associated with successful implementation.

Blood pressure stratification using photoplethysmography and light gradient boosting machine

Introduction: Globally, hypertension (HT) is a substantial risk factor for cardiovascular disease and mortality; hence, rapid identification and treatment of HT is crucial. In this study, we tested the light gradient boosting machine (LightGBM) machine learning method for blood pressure stratification based on photoplethysmography (PPG), which is used in most wearable devices. Methods: We used 121 records of PPG and arterial blood pressure (ABP) signals from the Medical Information Mart for Intensive Care III public database. PPG, velocity plethysmography, and acceleration plethysmography were used to estimate blood pressure; the ABP signals were used to determine the blood pressure stratification categories. Seven feature sets were established and used to train the Optuna-tuned LightGBM model. Three trials compared normotension (NT) vs. prehypertension (PHT), NT vs. HT, and NT + PHT vs. HT. Results: The F1 scores for these three classification trials were 90.18%, 97.51%, and 92.77%, respectively. The results showed that combining multiple features from PPG and its derivative led to a more accurate classification of HT classes than using features from only the PPG signal. Discussion: The proposed method showed high accuracy in stratifying HT risks, providing a noninvasive, rapid, and robust method for the early detection of HT, with promising applications in the field of wearable cuffless blood pressure measurement.

Laser-Induced Graphene for Heartbeat Monitoring with HeartPy Analysis

The HeartPy Python toolkit for analysis of noisy signals from heart rate measurements is an excellent tool to use in conjunction with novel wearable sensors. Nevertheless, most of the work to date has focused on applying the toolkit to data measured with commercially available sensors. We demonstrate the application of the HeartPy functions to data obtained with a novel graphene-based heartbeat sensor. We produce the sensor by laser-inducing graphene on a flexible polyimide substrate. Both graphene on the polyimide substrate and graphene transferred onto a PDMS substrate show piezoresistive behavior that can be utilized to measure human heartbeat by registering median cubital vein motion during blood pumping. We process electrical resistance data from the graphene sensor using HeartPy and demonstrate extraction of several heartbeat parameters, in agreement with measurements taken with independent reference sensors. We compare the quality of the heartbeat signal from graphene on different substrates, demonstrating that in all cases the device yields results consistent with reference sensors. Our work is a first demonstration of successful application of HeartPy to analysis of data from a sensor in development.

Heartbeat detection from the upper arm using an SWT-based zero-phase filter bank incorporated with a voting Scheme

Electrocardiogram (ECG) signal provides a graphical representation of cardiac activity and is the most commonly adopted clinical tool for cardiac abnormalities detection. Heartbeat detection, as the first step in analyzing ECG signals, is required for an accurate diagnosis. Stationary wavelet transform (SWT) as a commonly used algorithm for heartbeat detection has a disadvantage of phase shift regarding the original signal. This work addresses this issue by presenting a new method that incorporates an SWT-based zero-phase filter bank with a voting scheme. Our results indicated that a superior performance in heartbeat detection was achieved from the upper arm compared to conventional SWT with a more accurate localization. We achieved sensitivity (SE) and positive predictive value (PPV) of 0.98±0.04 and 0.95±0.09 with the most distance of 50 ms from the actual heartbeats. The SE and PPV changed to 0.75±0.15 and 0.73±0.16, respectively for the distance of 20 ms. Clinical Relevance- The proposed method can be later implemented in wearable devices for convenient cardiac activity monitoring from upper arm or other none-conventional sites.

Low cost, non-invasive, and continuous vital signs monitoring device for pregnant women in low resource settings (Lvital device)

Around 800 women die each day from complications of pregnancy and childbirth in the world. Vital Signs monitoring (such as blood pressure, pulse rate, and temperature) are among fundamental parameters of ensuring the health and safety of women and newborns during pregnancy, labor, and childbirth. Approximately, 10% of women experience hypertension (greater than140/90) during pregnancy. High blood pressure during pregnancy can cause extra stress on the heart and kidneys and can increase the risk of heart disease. Therefore, early recognition of abnormal vital signs, which are induced due to pregnancy can allow for timely identification of clinical deterioration. Currently used technologies are expensive and complex design with implementation challenges in low-resource settings where maternal morbidity and mortality is higher. Thus, considering the above need, here a hardware device has been designed and developed, which is a low cost, and portable for pregnant women's vital signs (with cuff-less blood pressure, heart rate, and body temperature) monitoring device. The developed device would have a remarkable benefit of monitoring the maternal vital signs especially for those in low resource settings, where there is a high paucity of vital signs monitoring devices.

Wearable Photoplethysmography for Cardiovascular Monitoring

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.

Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: A review from VascAgeNet

The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.

A Review of the Commercially Available ECG Detection and Transmission Systems-The Fuzzy Logic Approach in the Prevention of Sudden Cardiac Arrest

Sudden cardiac death (SCD) constitutes a major clinical and public health problem, whose death burden is comparable to the current worldwide pandemic. This comprehensive review encompasses the following topics: available rescue systems, wearable electrocardiograms (ECG), detection and transmission technology, and a newly developed fuzzy logic algorithm (FA) for heart rhythm classification which is state-of-the art in the field of SCD prevention. Project “PROTECTOR”, the Polish Rapid Transtelephonic ECG to Obtain Resuscitation for development of a rapid rescue system for patients at risk of sudden cardiac arrest (SCA), is presented. If a lethal arrhythmia is detected on the basis of FA, the system produces an alarm signal audible for bystanders and transmits the alarm message along with location to the emergency medical center. Phone guided resuscitation can be started immediately because an automated external defibrillator (AED) localization map is available. An automatic, very fast diagnosis is a unique feature of the PROTECTOR prototype. The rapid detection of SCA is based on a processor characterized by 100% sensitivity and 97.8% specificity (as measured in the pilot studies). An integrated circuit which implements FA has already been designed and a diagnosis is made within few seconds, which is extremely important in ischemic brain damage prophylaxis. This circuit could be implemented in smart implants (Sis).

Blood pressure assessment with in-ear photoplethysmography

Objective. In this study, we aimed to estimate blood pressure (BP) from in-ear photoplethysmography (PPG). This novel implementation provided an unobtrusive and steady way of recording PPG, whereas previous PPG measurements were mostly performed at the wrist, finger, or earlobe.Methods. The time between forward and reflected PPG waves was very short at the ear site. To minimize errors introduced by feature extraction, a multi-Gaussian decomposition of in-ear PPG was performed. Both hand-crafted and whole-based features were extracted and the best combination of features was selected using a backward-search wrapper method and evaluated by the Akaike information criteria. Hemodynamic parameters such as compliance and inertance were estimated from a four-element Windkessel (WK4) model, which was used to pre-classify PPG signals and generate different BP estimation algorithms. Calibration was done by using previous measurements from the same class. To validate this novel approach, 53 subjects were recruited for a one-month follow-up study, and 17 subjects were recruited for a two-month follow-up study. Calibrated systolic BP estimation accuracy was significantly improved with inertance-based pre-classification, while diastolic BP showed less improvement.Results. With proper feature selection, pre-classification and calibration, we have achieved a mean absolute error of 5.35 mmHg for SBP estimation, compared to 6.16 mmHg if no pre-classification was carried out. The performance did not deteriorate in two months, showing a decent BP trend-tracking ability.Conclusion. The study demonstrated the feasibility of in-ear PPG to reliably measure BP, which represents an important technological advancement in terms of unobtrusiveness and steadiness.

A Pilot Study of Blood Pressure Monitoring After Cardiac Surgery Using a Wearable, Non-invasive Sensor

Background: Continuous blood pressure (BP) measurement in intensive care units is based on arterial line (AL) transducers, sometimes associated with clinical complications. Our objective was to evaluate continuous BP measurements obtained from a non-invasive, wireless photoplethysmography (PPG)-based device using two distinct configurations (wristwatch and chest-patch monitors) compared to an AL. Methods: In this prospective evaluation study, comparison of the PPG-based devices to the AL was conducted in 10 patients immediately following cardiac surgery. Pulse rate (PR), systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) were recorded using both the AL and the PPG-based devices simultaneously for an average of 432 ± 290 min starting immediately after cardiac surgery. Bland-Altman plots and Pearson's correlations were used to assess the accuracy and degree of agreement between techniques. Results: A total of ~4,000 data points were included in the final analysis. AL measurements for PR, SBP, DBP and MAP were significantly (p < 0.001) and strongly correlated with both the wristwatch (r = 0.99, r = 0.94, r = 0.93 and r = 0.96, respectively) and the chest-patch (r = 0.99, r = 0.95, r = 0.93 and r = 0.95, respectively) monitors. Both configurations showed a marginal bias of <1 mmHg for BP measurements and <1 beat/min for PR [95% limits of agreement -3,3 beat/min; BP measurements: (-6)-(-10), 6-10 mmHg] compared to AL measurements. Conclusion: The PPG-based devices offer a high level of accuracy for cardiac-related parameters compared to an AL in post-cardiac surgery patients. Such devices could provide advanced monitoring capabilities in a variety of clinical settings, including immediate post-operative and intensive care unit settings. Clinical Trial Registration:www.clinicaltrials.gov, NCT03603860.

Non-invasive cuff-less blood pressure machine learning algorithm using photoplethysmography and prior physiological data

Conventional blood pressure (BP) measurement methods have a number of drawbacks such as being invasive, cuff-based or requiring manual operation. Many studies are focussed on emerging methods of noninvasive, cuff-less and continuous BP measurement, and using only photoplethysmography to estimate BP has become popular. Although it is well known that physiological characteristics of the subject are important in BP estimation, this has not been widely explored. This article presents a novel method which adopts photoplethysmography and prior knowledge of a subject's physiological features to estimate DBP and SBP. Features extracted from a fingertip photoplethysmography signal and prior knowledge of a subject's physiological characteristics, such as gender, age, height, weight and BMI is used to estimate BP using three different machine learning models: artificial neural networks, support vector machine and least absolute shrinkage and selection operator regression. The accuracy of BP estimation obtained when prior knowledge of the physiological characteristics are incorporated into the model is superior to those which do not take the physiological characteristics into consideration. In this study, the best performing algorithm is an artificial neural network which obtains a mean absolute error and SD of 4.74 ± 5.55 mm Hg for DBP and 9.18 ± 12.57 mm Hg for SBP compared to 6.61 ± 8.04 mm Hg for DBP and 11.12 ± 14.20 mm Hg for SBP without prior knowledge. The inclusion of prior knowledge of the physiological characteristics can improve the accuracy of BP estimation using machine learning methods, and the incorporation of more physiological characteristics enhances the accuracy of the BP estimation.

PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals

An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.

Learning and non-learning algorithms for cuffless blood pressure measurement: a review

The machine learning approach has gained a significant attention in the healthcare sector because of the prospect of developing new techniques for medical devices and handling the critical database of chronic diseases. The learning approach has potential to analyze complex medical data, disease diagnosis, and patient monitoring system, and to monitor e-health record. Non-invasive cuffless blood pressure (CLBP) measurement secured a significant position in the patient monitoring system. From a few recent decades, the importance of cuffless technology has been perceived towards continuous monitoring of blood pressure (BP) and supplementary efforts have been made towards its continuous monitoring. However, the optimal method that measures BP unambiguously and continuously has not yet emerged along with issues like calibration time, accuracy and long-term estimation of BP with miniaturizing hardware. The present study provides an insight into several learning algorithms along with their feature selection models. Various challenges and future improvements towards the current state of machine learning in healthcare industries are discussed in the present review. The bottom line of this study is to provide a comprehensive perspective of the machine learning approach of CLBP for the generation of highly precise predictive models for continuous BP measurement.

A correlation study of beat-to-beat R-R intervals and pulse arrival time under natural state and cold stimulation

Beat-to-beat R-R intervals (RRI) and pulse arrival time (PAT) provide pivotal information to evaluate cardiac autonomic functions for predicting arrhythmias and cardiovascular morbidity. However, their relationship has not been clearly understood. In this study, we simultaneously recorded electrocardiograms and photoplethysmograms on 34 subjects in the natural state, and on 55 subjects under the cold stimulation. The RRI and the PAT were calculated and then analyzed using Pearson correlation coefficient. The results showed that the RRI and the PAT were strongly correlated (r = 0.562) and the RRI series were 2.18 ± 0.40 beats advanced to the PAT series. After smoothing, the RRI and the PAT were more correlated in the low frequency than in the high frequency. Furthermore, when involving RRI with the phase effect, the proposed PAT based model showed better performance for blood pressure estimation. We think these results are helpful to understand the underlying regulatory mechanisms of the two cardiovascular factors, and would provide useful suggestions for non-invasive cuffless blood pressure estimation.

Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives

According to the WTO, there were 1.13 billion hypertension patients worldwide in 2015. The WTO encouraged people to check the blood pressure regularly because a large amount of patients do not have any symptoms. However, traditional cuff measurement results are not enough to represent the patient′s blood pressure status over a period of time. Therefore, there is an urgent need for portable, easy to operate, continuous measurement, and low-cost blood pressure measuring devices. In this paper, we adopted the convolutional neural network (CNN), based on the Hilbert–Huang Transform (HHT) method, to predict blood pressure (BP) risk level using photoplethysmography (PPG). Considering that the PPG′s first and second derivative signals are related to atherosclerosis and vascular elasticity, we created a dataset called PPG+; the images of PPG+ carry information on PPG and its derivatives. We built three classification experiments by collecting 582 data records (the length of each record is 10 s) from the Medical Information Mart for Intensive Care (MIMIC) database: NT (normotension) vs. HT (hypertension), NT vs. PHT (prehypertension), and (NT + PHT) vs. HT; the F1 scores of the PPG + experiments using AlexNet were 98.90%, 85.80%, and 93.54%, respectively. We found that, first, the dataset established by the HHT method performed well in the BP grade prediction experiment. Second, because the Hilbert spectra of the PPG are simple and periodic, AlexNet, which has only 8 layers, got better results. More layers instead increased the cost and difficulty of training.

Wearables bei Epilepsien

Epileptische Anfälle führen zu verschiedensten körperlichen Symptomen, die je nach Art und Ausprägung mit geeigneten Geräten gemessen werden und als Surrogatmarker epileptischer Anfälle dienen können. Dominierende motorische Symptome können mit Beschleunigungssensoren oder elektromyografisch erfasst werden. Bei fokalen Anfällen mit fehlender oder geringer motorischer Beteiligung können autonome Phänomene wie Änderungen der Herzrate, Atmung und des elektrischen Hautwiderstandes per Elektrokardiografie, Photopletysmografie und Hautsensoren gemessen werden. Die in den heutigen Wearables integrierten Sensoren können diese Körpersignale messen und zur automatisierten Anfallserkennung nutzbar machen. In dieser Übersichtsarbeit werden verschiedene Sensortechnologien, Wearables und deren Anwendung zur automatisierten Erkennung epileptischer Anfälle vorgestellt, Gütekriterien zur Einschätzung mobiler Gesundheitstechnologien diskutiert und klinisch geprüfte Systeme zusammengefasst.

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

Novel Digital Technologies for Blood Pressure Monitoring and Hypertension Management

PURPOSE OF REVIEW

Hypertension is common, impacting an estimated 108 million US adults, and deadly, responsible for the deaths of one in six adults annually. Optimal management includes frequent blood pressure monitoring and antihypertensive medication titration, but in the traditional office-based care delivery model, patients have their blood pressure measured only intermittently and in a way that is subject to misdiagnosis with white coat or masked hypertension. There is a growing opportunity to leverage our expanding repository of digital technology to reimagine hypertension care delivery. This paper reviews existing and emerging digital tools available for hypertension management, as well as behavioral economic insights that could supercharge their impact.

RECENT FINDINGS

Digitally connected blood pressure monitors offer an alternative to office-based blood pressure monitoring. A number of cuffless blood pressure monitors are in development but require further validation before they can be deployed for widespread clinical use. Patient-facing hubs and applications offer a means to transmit blood pressure data to clinicians. Though artificial intelligence could allow for curation of this data, its clinical use for hypertension remains limited to assessing risk factors at this time. Finally, text-based and telemedicine platforms are increasingly being employed to translate hypertension data into clinical outcomes with promising results.

SUMMARY

The digital management of hypertension shows potential as an avenue for increasing patient engagement and improving clinical efficiency and outcomes. It is important for clinicians to understand the benefits, limitations, and future directions of digital health to optimize management of hypertension.

Validation of a wearable cuff-less wristwatch-type blood pressure monitoring device

Ambulatory blood pressure (BP) monitoring is recommended to improve the management of hypertension. Here, we investigated the accuracy of BP estimated using a wearable cuff-less device, InBodyWATCH, compared with BP measured using a manual sphygmomanometer. Thirty-five adults were enrolled (age 57.1 ± 17.9 years). The BP was estimated using InBodyWATCH with an individualized estimation based on a neural network model. Three paired sets of BPs from the two devices were compared using correlation analysis and Bland–Altman plots (n = 105 paired BP readings). The correlations for both systolic and diastolic BP (SBP and DBP) between the two devices were high (r = 0.964 and 0.939, both P < 0.001). The mean difference was 2.2 ± 6.1 mmHg for SBP and −0.2 ± 4.2 mmHg for DBP; these were not significant (P = 0.472 for SBP and P = 0.880 for DBP). The proportions of estimated SBP/DBP obtained from the InBodyWATCH within ± 5 mmHg of manual SBP/DBP were 71.4%/83.8%; within ± 10 mmHg they were 86.7%/98.1%; and within ± 15 mmHg they were 97.1%/99.0%. The estimated BP from this wearable cuff-less device correlated highly with the manual BP and showed good accuracy, suggesting its potential to be used in ambulatory BP monitoring.

Patch validation: an observational study protocol for the evaluation of a multisignal wearable sensor in patients during anaesthesia and in the postanaesthesia care unit

INTRODUCTION

Except for operating rooms, postanaesthesia care units and intensive care units, where the monitoring of vital signs is continuous, intermittent care is standard practice. However, at a time when only the patients with the most serious conditions are hospitalised and only a fraction of these patients are in intensive care units, this type of monitoring is no longer sufficient. Wireless monitoring has been proposed, but it requires rigorous validation. The aim of this observational study is to compare vital signs obtained from a precordial patch sensor to those obtained with conventional monitoring.

METHODS AND ANALYSIS

This patch validation trial will be an observational, prospective, single-centre open study of 115 anaesthetised adult patients monitored with both a wireless sensor (myAngel VitalSigns, Devinnova, Montpellier, France) and a standard bedside monitor (Carescape Monitor B850, GE Healthcare, Chicago, Illinois). Both sensors will be used to record peripheral oxygen saturation, respiratory rate, heart rate, body temperature and blood pressure (systolic and diastolic). The main objective will be to assess the degree of agreement between the two systems during the patients' stay in the postanaesthesia care unit, both at the raw signal level and at the clinical parameter level. The secondary objectives will be to assess the same performance under anaesthesia, the frequency of missing data or artefacts, the diagnostic performance of the systems, the influence of patients' characteristics on agreement between the two systems, the adverse events and the acceptability of the patch to patients. Bland-Altman plots will be used in the main analysis to detect discrepancies and estimate the limits of agreement.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the Ethical Committee (Toulouse, France) on 10 April 2020. We are not yet recruiting subjects for this study. The results will be submitted for publication in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04344093.

Synthetic photoplethysmogram generation using two Gaussian functions

Evaluating the performance of photoplethysmogram (PPG) event detection algorithms requires a large number of PPG signals with different noise levels and sampling frequencies. As publicly available PPG databases provide few options, artificially constructed PPG signals can also be used to facilitate this evaluation. Here, we propose a dynamic model to synthesize PPG over specified time durations and sampling frequencies. In this model, a single pulse was simulated by two Gaussian functions. Additionally, the beat-to-beat intervals were simulated using a normal distribution with a specific mean value and a specific standard deviation value. To add periodicity and to generate a complete signal, the circular motion principle was used. We synthesized three classes of pulses by emulating three different templates: excellent (systolic and diastolic waves are salient), acceptable (systolic and diastolic waves are not salient), and unfit (systolic and diastolic waves are noisy). The optimized model fitting of the Gaussian functions to the templates yielded 0.99, 0.98, and 0.85 correlations between the template and synthetic pulses for the excellent, acceptable, and unfit classes, respectively, with mean square errors of 0.001, 0.003, and 0.017, respectively. By comparing the heart rate variability of real PPG and randomly synthesized PPG for 5 min in 116 records from the MIMIC III database, strong correlations were found in SDNN, RMSSD, LF, HF, SD1, and SD2 (0.99, 0.89, 0.84, 0.89, 0.90 and 0.95, respectively).

Management of Hypertension in the Digital Era: Small Wearable Monitoring Devices for Remote Blood Pressure Monitoring

Out-of-office blood pressure measurement is an essential part of diagnosing and managing hypertension. In the era of advanced digital health information technology, the approach to achieving this is shifting from traditional methods (ambulatory and home blood pressure monitoring) to wearable devices and technology. Wearable blood pressure monitors allow frequent blood pressure measurements (ideally continuous beat-by-beat monitoring of blood pressure) with minimal stress on the patient. It is expected that wearable devices will dramatically change the quality of detection and management of hypertension by increasing the number of measurements in different situations, allowing accurate detection of phenotypes that have a negative impact on cardiovascular prognosis, such as masked hypertension and abnormal blood pressure variability. Frequent blood pressure measurements and the addition of new features such as monitoring of environmental conditions allows interpretation of blood pressure data in the context of daily stressors and different situations. This new digital approach to hypertension contributes to anticipation medicine, which refers to strategies designed to identify increasing risk and predict the onset of cardiovascular events based on a series of data collected over time, allowing proactive interventions to reduce risk. To achieve this, further research and validation is required to develop wearable blood pressure monitoring devices that provide the same accuracy as current approaches and can effectively contribute to personalized medicine.

Using pulse oximetry waveforms to detect coarctation of the aorta

Background

Coarctation of the aorta is a common form of critical congenital heart disease that remains challenging to diagnose prior to clinical deterioration. Despite current screening methods, infants with coarctation may present with life-threatening cardiogenic shock requiring urgent hospitalization and intervention. We sought to improve critical congenital heart disease screening by using a novel pulse oximetry waveform analysis, specifically focused on detection of coarctation of the aorta.

Methods and results

Over a 2-year period, we obtained pulse oximetry waveform data on 18 neonates with coarctation of the aorta and 18 age-matched controls hospitalized in the cardiac intensive care unit at Children’s Healthcare of Atlanta. Patients with coarctation were receiving prostaglandin E1 and had a patent ductus arteriosus. By analyzing discrete features in the waveforms, we identified statistically significant differences in the maximum rate of fall between patients with and without coarctation. This was accentuated when comparing the difference between the upper and lower extremities, with the lower extremities having a shallow slope angle when a coarctation was present (p-value 0.001). Postoperatively, there were still differences in the maximum rate of fall between the repaired coarctation patients and controls; however, these differences normalized when compared with the same individual’s upper vs. lower extremities. Coarctation patients compared to themselves (preoperatively and postoperatively), demonstrated waveform differences between upper and lower extremities that were significantly reduced after successful surgery (p-value 0.028). This screening algorithm had an accuracy of detection of 72% with 0.61 sensitivity and 0.94 specificity.

Conclusions

We were able to identify specific features in pulse oximetry waveforms that were able to accurately identify patients with coarctation and further demonstrated that these changes normalized after surgical repair. Pulse oximetry screening for congenital heart disease in neonates may thus be improved by including waveform analysis, aiming to identify coarctation of the aorta prior to critical illness. Further large-scale testing is required to validate this screening model among patients in a newborn nursery setting who are low risk for having coarctation.

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.

Novel Deep Convolutional Neural Network for Cuff-less Blood Pressure Measurement Using ECG and PPG Signals

Cuff-less blood pressure (BP) is a potential method for BP monitoring because it is undisturbed and continuous monitoring. Existing cuff-less estimation methods are easily influenced by signal noise and non-ideal signal morphology. In this study we propose a novel well-designed Convolutional Neural Network (CNN) model named Deep-BP for BP estimation task. The structure of Deep-BP can help to capture more underlying data features associated with BP than handcrafted features, thus improving the robustness and estimation accuracy. We carry out experiments with and without calibration procedure in training stage to evaluate the performance of new method in different application scenarios. The experiment results show that the Deep-BP model achieves high accuracy and outperforms existing methods, in the experiments both with and without calibration.

Recent advances of biosensors for hypertension and nephrology

PURPOSE OF REVIEW

Hypertension (HTN) and chronic kidney disease (CKD) are significant problems. With recent advances in technologies, biosensors have shown a great potential to provide better home monitoring in hypertension (HTN), medication compliance, diagnostic device for kidney disease, CKD/end-stage renal disease (ESRD) care, and post kidney transplant management.

RECENT FINDINGS

Multiple devices/biosensors have been developed related to HTN, kidney function including real-time glomerular filtration rate, CKD/end-stage renal disease, and transplant care. In recent advances in wearable biosensors, point of care monitoring system could provide more integrated care to the patients via telenephrology.

SUMMARY

This review focuses on the recent advances in biosensors which may be useful for HTN and nephrology. We will discuss future potential clinical implication of these biosensors.

Evaluation of transit time-based models in wearable central aortic blood pressure estimation

Evidence suggests that central aortic blood pressure (CABP) may provide a more accurate prognosis of cardiovascular events than peripheral pressure. The capability of monitoring CABP in a continuous, wearable, unobtrusive way might have a significant impact on hypertension management. The purpose of this study is to experimentally explore whether a wearable device equipped with an electrocardiogram (ECG) and ballistocardiogram (BCG) acquisition system could be used to predict CABP. This is based on state-of-the-art results on the relationship between transit time extracted from these signals and CABP. Ten young, healthy volunteers participated in the study where data-sets were acquired during three hemodynamic interventions, i.e., breath-holding, Valsalva maneuver, and cold pressor. Each data-set included ECG and BCG waveforms acquired by the wearable device and a CABP assessment from a cuff-based device. A total of nine PTT-based models (PBMs) derived from pulse transit time methodology were considered. Each PBM was tested with three alternative feature times extracted from the recorded waveforms PBMs were calibrated with data-sets acquired at baseline state, which were not considered for testing the PBM estimation performance. Four of the nine tested models presented a proper agreement in estimating CABP through the acquired signals, after the calibration procedure with baseline-state data. Results in one of these promising models are the following. Mean estimation error (95% confidence interval), systolic: 0 to 1.7 mmHg, diastolic: 0.4 to 2.3 mmHg, Pearson correlation: 0.82 systolic and 0.78 diastolic (p < 0.001). The proposed methodology may lead to continuous wearable BP monitoring.

Phase-domain Deep Patient-ECG Image Learning for Zero-effort Smart Health Security

Smart health is quickly boosted by technological advancements: smart sensors, body sensor network, internet of medical things and big data. Vast amounts of smart health big data from ubiquitous sensors pose unprecedented challenges to the security and privacy protection, which is extremely critical in healthcare applications. The vital signs, user daily behaviors, medicine recommendations, and so many other health data are vulnerable to different attacks, due to the fact that wearable/mobile monitors have very strict performance/power constraints, which limit the complexity of security protocols. In this paper, we study how to leverage a natural vital signal (Electrocardiogram - ECG) for user identification purpose, without introducing new hardware sensing devices. ECG is not only a gold standard cardiac signal, but also unique to each individual. We investigate a phase-domain deep patient-ECG image learning framework, to tackle key challenges in ECG biometric user identification: high diversities of ECG morphologies due to heart diseases, and time-consuming/ineffective heartbeat localization methods & manual feature engineering. The ultimate goal is to make the smart health security zero-effort: use `phase-domain transformation' to enable blind signal segmentation without localizing heartbeats; create a computer image processing-like task by `pixelating' phase-domain ECG trajectories to ECG images; and enable automatic (non-manual) `deep feature learning' using a deep convolutional neural network. Evaluated on two patient-ECG databases, this zero-effort framework achieves an accuracy as high as 97.2%, and greatly outperforms state-of-the-art studies in terms of the generalization ability and/or performance. This study is expected to enable highly challenging patient-ECG biometric user identification, by generalizable blind signal segmentation and deep feature learning strategies, in the era of smart health boosted by internet of medical things and big medical data.

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.

Innovative Use of Wrist-Worn Wearable Devices in the Sports Domain: A Systematic Review

Wrist wearables are becoming more and more popular, and its use is widespread in sports, both professional and amateur. However, at present, they do not seem to exploit all their potential. The objective of this study is to explore innovative proposals for the use of wearable wrist technology in the field of sports, to understand its potential and identify new challenges and lines of future research related to this technology. A systematic review of the scientific literature, collected in 4 major repositories, was carried out to locate research initiatives where wrist wearables were introduced to address some sports-related challenges. Those works that were limited to evaluating sensor performance in sports activities and those in which wrist wearable devices did not play a significant role were excluded. 26 articles were eventually selected for full-text analysis that discuss the introduction of wrist-worn wearables to address some innovative use in the sports field. This study showcases relevant proposals in 10 different sports. The research initiatives identified are oriented to the use of wearable wrist technology (i) for the comprehensive monitoring of sportspeople’s behavior in activities not supported by the vendors, (ii) to identify specific types of movements or actions in specific sports, and (iii) to prevent injuries. There are, however, open issues that should be tackled in the future, such as the incorporation of these devices in sports activities not currently addressed, or the provision of specific recommendation services for sport practitioners.

The use of photoplethysmography for assessing hypertension

The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.

New Approaches in Hypertension Management: a Review of Current and Developing Technologies and Their Potential Impact on Hypertension Care

Abstract

Hypertension is a key risk factor for cardiovascular disease. Currently, around a third of people with hypertension are undiagnosed, and of those diagnosed, around half are not taking antihypertensive medications. The World Health Organisation (WHO) estimates that high blood pressure directly or indirectly causes deaths of at least nine million people globally every year.

Purpose of Review

In this review, we examine how emerging technologies might support improved detection and management of hypertension not only in the wider population but also within special population groups such as the elderly, pregnant women, and those with atrial fibrillation.

Recent Findings

There is an emerging trend to empower patients to support hypertension screening and diagnosis, and several studies have shown the benefit of tele-monitoring, particularly when coupled with co-intervention, in improving the management of hypertension.

Summary

Novel technology including smartphones and Bluetooth®-enabled tele-monitoring are evolving as key players in hypertension management and offer particular promise within pregnancy and developing countries. The most pressing need is for these new technologies to be properly assessed and clinically validated prior to widespread implementation in the general population.

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.

Algorithm and VLSI Architecture Design of Low-Power SPIHT Decoder for mHealth Applications

A real-time cost and power-efficient (CPE) set partitioning in hierarchical trees (SPIHT) decoder design with low hardware complexity and low-power dissipation is introduced in one-dimension (1-D) wavelet-based quality-assured electrocardiograph (ECG) compression systems for mobile health (mHealth) applications. However, current SPIHT coding architectures are designed for image/video processing. These architectures require a large amount of memory as well as complicated sorting algorithms, which both require time-consuming tasks and are unsuitable for mobile ECG applications. Based on our previously modified SPIHT coding work, which used flags and check bits to reduce memory requirements and coding complexity by merging three search processes into one step. Therefore, to achieve the real-time design goal for mobile ECG applications, in this paper, we first introduce a hardware-oriented SPIHT decoding algorithm that is suitable for decoding the previously presented SPIHT coding work. Accordingly, an appropriate low-power hardware architecture is developed to implement a real-time high-performance and low-cost SPIHT VLSI design for our proposed decoder algorithm, which is appropriate for mobile ECG applications. Using the distinct ECG signals in the MIT-BIH arrhythmia database (sampling rate of 360 Hz), the final simulation and VLSI implementation results reveal that the proposed CPE SPIHT decoder design outperforms the state-of-the-art designs in terms of the average decoding time, the decoding quality, the VLSI speed, and the power consumption. Most importantly, the design can be exploited to a 1-D 1024 × 1 wavelet-based quality-assured ECG data compression system.

Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification

Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals.

Multi-Wavelength Photoplethysmography Enabling Continuous Blood Pressure Measurement With Compact Wearable Electronics

OBJECTIVE

To fight the "silent killer" hypertension, continuous blood pressure (BP) monitoring has been one of the most desired functions in wearable electronics. However, current BP measuring principles and protocols either involve a vessel occlusion process with a cuff or require multiple sensing nodes on the body, which makes it difficult to implement them in compact wearable electronics like smartwatches and wristbands with long-term wearability.

METHODS

In this work, we proposed a highly compact multi-wavelength photoplethysmography (MWPPG) module and a depth-resolved MWPPG approach for continuous monitoring of BP and systemic vascular resistance (SVR). By associating the wavelength-dependent light penetration depth in the skin with skin vasculatures, our method exploited the pulse transit time (PTT) on skin arterioles for tracking SVR (n = 20). Then, we developed an arteriolar PTT-based method for beat-to-beat BP measurement. The BP estimation accuracy of the proposed arteriolar PTT method was validated against Finometer (n = 20) and the arterial line (n = 4).

RESULTS

The correlation between arteriolar PTT and SVR was theoretically deduced and experimentally validated on 20 human subjects performing various maneuvers. The proposed arteriolar PTT-based method outperformed the traditional arterial PTT-based method with better BP estimation accuracy and simpler measurement setup, i.e., with a single sensing node.

CONCLUSION

The proposed depth-resolved MWPPG method can provide accurate measurements of SVR and BP, which are traditionally difficult to measure in a noninvasive or continuous fashion.

SIGNIFICANCE

This MWPPG work provides the wearable healthcare electronics of compact size with a low-cost and physiology-based solution for continuous measurement of BP and SVR.

Arterial blood pressure feature estimation using photoplethysmography

Continuous and noninvasive monitoring of blood pressure has numerous clinical and fitness applications. Current methods of continuous measurement of blood pressure are either invasive and/or require expensive equipment. Therefore, we investigated a new method for the continuous estimation of two main features of blood pressure waveform: systolic and diastolic pressures. The estimates were obtained from a photoplethysmography signal as input to the fifth order autoregressive moving average models. The performance of the method was evaluated using beat-to-beat full-wave blood pressure measurements from 15 young subjects, with no known cardiovascular disorder, in supine position as they breathed normally and also while they performed a breath-hold maneuver. The level of error in the modeling and prediction estimates during normal breathing and breath-hold maneuvers, as measured by the root mean square of the residuals, were less than 5 mmHg and 11 mm Hg, respectively. The mean of model residuals both during normal breathing and breath-hold maneuvers was considered to be less than 3.2 mmHg. The dependency of the accuracy of the estimates on the subject data was assessed by comparing the modeling errors for the 15 subjects. Less than 1% of the models showed significant differences (p < 0.05) from the other models, which indicates a high level of consistency among the models.

Post marketing study of hemodynamic and hematological noninvasive readings in a blood bank

Objectives:

This validation test was conducted in the Fujisan Blood Bank, Fortaleza, Brazil and evaluated the noninvasive TensorTip MTX (MTX, Cnoga Medical Ltd.) readings of hemoglobin, hematocrit, red blood cells, blood pressure, and heart rate compared to reference lab device readings. Generally, these parameters are measured from venous or capillary blood samples run on a laboratory analyzer or handheld invasive testing devices. Needle sticks are inconvenient to blood donors with relatively high exposure risks. To our vision, noninvasive determination would be of benefit to blood contributors and medical professional teams; it would be fast and painless.

Methods:

A total of 334 subjects were included in the Fujisan blood bank validation (65% male, 35% female). Hemoglobin, hematocrit, and red blood cells, as well as blood pressure and heart rate, were measured noninvasively using the MTX device and were compared to venous blood samples run on two laboratory hematology analyzers (Horiba ABX Micros60 and Siemens blood count analyzer), to digital sphygmomanometer (OMRON BP786) and to manual auscultation. The noninvasive measurement with the appropriate virtual arm cuff setting was performed simultaneously with the blood sample extraction of the reference devices measurement.

Results:

There was no statistically significant difference (p > 0.05, paired, two-tailed t-test) between the average daily hemoglobin, hematocrit, and red blood cells measurements provided by the MTX device and the laboratory hematology analyzer. In addition, there was no significant difference between the daily blood pressure and heart rate results provided by the MTX device and the digital and manual sphygmomanometers. The error calculated between the MTX and the reference device was found to be sufficiently accurate according to the relevant standards.

Conclusion:

The MTX accuracy of noninvasive hemoglobin, hematocrit, red blood cells, blood pressure, and heart rate measurements satisfies the industrial standards; therefore, the device enables more accurate, efficient, and effective patient care.

Artificial intelligence and machine learning in emergency medicine

Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power. AI is proving to be increasingly applicable to healthcare and there is a growing list of tasks where algorithms have matched or surpassed physician performance. Despite the successes there remain significant concerns and challenges surrounding algorithm opacity, trust and patient data security. Notwithstanding these challenges, AI technologies will likely become increasingly integrated into emergency medicine in the coming years. This perspective presents an overview of current AI research relevant to emergency medicine.