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

Applications of flexible electronics related to cardiocerebral vascular system

Ensuring accessible and high-quality healthcare worldwide requires field-deployable and affordable clinical diagnostic tools with high performance. In recent years, flexible electronics with wearable and implantable capabilities have garnered significant attention from researchers, which functioned as vital clinical diagnostic-assisted tools by real-time signal transmission from interested targets in vivo. As the most crucial and complex system of human body, cardiocerebral vascular system together with heart-brain network attracts researchers inputting profuse and indefatigable efforts on proper flexible electronics design and materials selection, trying to overcome the impassable gulf between vivid organisms and rigid inorganic units. This article reviews recent breakthroughs in flexible electronics specifically applied to cardiocerebral vascular system and heart-brain network. Relevant sensor types and working principles, electronics materials selection and treatment methods are expounded. Applications of flexible electronics related to these interested organs and systems are specially highlighted. Through precedent great working studies, we conclude their merits and point out some limitations in this emerging field, thus will help to pave the way for revolutionary flexible electronics and diagnosis assisted tools development.

Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure

Continuous monitoring of arterial blood pressure (BP) outside of a clinical setting is crucial for preventing and diagnosing hypertension related diseases. However, current continuous BP monitoring instruments suffer from either bulky systems or poor user-device interfacial performance, hampering their applications in continuous BP monitoring. Here, we report a thin, soft, miniaturized system (TSMS) that combines a conformal piezoelectric sensor array, an active pressure adaptation unit, a signal processing module, and an advanced machine learning method, to allow real wearable, continuous wireless monitoring of ambulatory artery BP. By optimizing the materials selection, control/sampling strategy, and system integration, the TSMS exhibits improved interfacial performance while maintaining Grade A level measurement accuracy. Initial trials on 87 volunteers and clinical tracking of two hypertension individuals prove the capability of the TSMS as a reliable BP measurement product, and its feasibility and practical usability in precise BP control and personalized diagnosis schemes development.

IoT Smart Flooring Supporting Active and Healthy Lifestyles

The lack of physical exercise is among the most relevant factors in developing health issues, and strategies to incentivize active lifestyles are key to preventing these issues. The PLEINAIR project developed a framework for creating outdoor park equipment, exploiting the IoT paradigm to build "Outdoor Smart Objects" (OSO) for making physical activity more appealing and rewarding to a broad range of users, regardless of their age and fitness. This paper presents the design and implementation of a prominent demonstrator of the OSO concept, consisting of a smart, sensitive flooring, based on anti-trauma floors commonly found in kids playgrounds. The floor is equipped with pressure sensors (piezoresistors) and visual feedback (LED-strips), to offer an enhanced, interactive and personalized user experience. OSOs exploit distributed intelligence and are connected to the Cloud infrastructure by using a MQTT protocol; apps have then been developed for interacting with the PLEINAIR system. Although simple in its general concept, several challenges must be faced, related to the application range (which called for high pressure sensitivity) and the scalability of the approach (requiring to implement a hierarchical system architecture). Some prototypes were fabricated and tested in a public environment, providing positive feedback to both the technical design and the concept validation.

Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL

AAL encompasses smart home technologies that are installed in the personal living environment in order to support older, disabled, as well as chronically ill people with the goal of delaying or reducing their need for nursing care in a care facility. Artificial intelligence (AI) is seen as an important tool for assisting the target group in their daily lives. A literature search and qualitative content analysis of 255 articles from computer science and engineering was conducted to explore the usage of ethical concepts. From an ethical point of view, the concept of independence and self-determination on the one hand and the possible loss of privacy on the other hand are widely discussed in the context of AAL. These concepts are adopted by the technical discourse in the sense that independence, self-determination and privacy are recognized as important values. Nevertheless, our research shows that these concepts have different usages and meanings in the ethical and the technical discourses. In the paper, we aim to map the different meanings of independence, self-determination and privacy as they can be found in the context of technological research on AI-based AAL systems. It investigates the interpretation of these ethical and social concepts which technicians try to build into AAL systems. In a second step, these interpretations are contextualized with concepts from the ethical discourse on AI-based assistive technologies.

Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing

Hypertension is the main cause of blood pressure (BP), which further causes various cardiovascular diseases (CVDs). The recent COVID-19 pandemic raised the burden on the healthcare system and also limits the resources to these patients only. The treatment of chronic patients, especially those who suffer from CVD, has fallen behind, resulting in increased deaths from CVD around the world. Regular monitoring of BP is crucial to prevent CVDs as it can be controlled and diagnosed through constant monitoring. To find an effective and convenient procedure for the early diagnosis of CVDs, photoplethysmography (PPG) is recognized as a low-cost technology. Through PPG technology, various cardiovascular parameters, including blood pressure, heart rate, blood oxygen saturation, etc., are detected. Merging the healthcare domain with information technology (IT) is a demanding area to reduce the rehospitalization of CVD patients. In the proposed model, PPG signals from the Internet of things (IoT)-enabled wearable patient monitoring (WPM) devices are used to monitor the heart rate (HR), etc., of the patients remotely. This article investigates various machine learning techniques such as decision tree (DT), naïve Bayes (NB), and support vector machine (SVM) and the deep learning model one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) to develop a system that assists physicians during continuous monitoring, which achieved an accuracy of 99.5% using PPG-BP data set. The proposed system provides cost-effective, efficient, and fully connected monitoring systems for cardiac patients.

Introduction of Boosting Algorithms in Continuous Non-Invasive Cuff-less Blood Pressure Estimation using Pulse Arrival Time

Blood Pressure (BP) is a critical biomarker for cardiorespiratory health. Conventional non-invasive BP measurement devices are mostly built on the principle of auscultation, oscillometry, or tonometry. The strong correlation between the Pulse Arrival Time (PAT) and BP has enabled unconstrained cuff-less BP monitoring. In this paper, we exploited that relationship for estimating Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial pressure (MAP) values. The proposed model involves extraction of PAT values by denoising the signals using advanced filtering techniques and finally employing machine learning algorithms to estimate cuff-less BP. The results are validated against Advancement of Medical Instrumentation (AAMI) standards and British Hypertension Society (BHS) protocols. The proposed method meets the AAMI standards in the context of estimating DBP and MAP values. The model's accuracy achieved Grade A for both MAP and DBP values using the CatBoost algorithm, whereas it achieved grade A for MAP and Grade B for DBP using the XGBoost algorithm based on the BHS standards.

Flexible Wearable Sensors for Cardiovascular Health Monitoring

Cardiovascular diseases account for the highest mortality globally, but recent advances in wearable technologies may potentially change how these illnesses are diagnosed and managed. In particular, continuous monitoring of cardiovascular vital signs for early intervention is highly desired. To this end, flexible wearable sensors that can be comfortably worn over long durations are gaining significant attention. In this review, advanced flexible wearable sensors for monitoring cardiovascular vital signals are outlined and discussed. Specifically, the functional materials, configurations, mechanisms, and recent advances of these flexible sensors for heart rate, blood pressure, blood oxygen saturation, and blood glucose monitoring are highlighted. Different mechanisms in bioelectric, mechano-electric, optoelectric, and ultrasonic wearable sensors are presented to monitor cardiovascular vital signs from different body locations. Present challenges, possible strategies, and future directions of these wearable sensors are also discussed. With rapid development, these flexible wearable sensors will potentially be applicable for both medical diagnosis and daily healthcare use in tackling cardiovascular diseases.

Evaluation of a novel mobile phone application for blood pressure monitoring: a proof of concept study

To provide information about the clinical relevance of blood pressure (BP) measurement differences between a new smartphone application (OptiBP™) and the reference method (automated oscillometric technique) using a noninvasive brachial cuff in patients admitted to the emergency department. We simultaneously recorded three BP measurements using both the reference method and the novel OptiBP™ (test method), except when the inter-arm difference was > 10 mmHg BP. Each OptiBP™ measurement required 1-min and the subsequent reference method values were compared to the values obtained with OptiBP™ using a Bland-Altman analysis and error grid analysis. Among the 110 patients recruited, OptiBP™ BP values could be collected on 61 patients (55%) and were included in the statistical analysis. The mean of differences (95% limits of agreement) between the reference method and the test method were - 0.1(- 22.5 to 22.4 mmHg) for systolic arterial pressure (SAP), - 0.1(- 12.9 to 12.7 mmHg) for diastolic arterial pressure (DAP) and - 0.3(- 18.1 to 17.4 mmHg) for mean arterial pressure (MAP). The proportions of measurements in risk zones A-E were 86.9%, 13.1%, 0%, 0%, and 0% for MAP and 89.3%, 10.7%, 0%, 0%, and 0% for SAP. In this pilot study conducted in stable and awake patients admitted to the emergency department, the absolute agreement between the OptiBP™ and the reference method was moderate. However, when BP measurements were made immediately after an initial calibration, error grid analysis showed that 100% of measurement differences between the OptiBP™ and reference method were categorized as no- or low-risk treatment decisions for all patients.Trial Registration: ClinicalTrials.gov Identifier: NCT04121624.

Advances in Non-Invasive Blood Pressure Monitoring

This paper reviews recent advances in non-invasive blood pressure monitoring and highlights the added value of a novel algorithm-based blood pressure sensor which uses machine-learning techniques to extract blood pressure values from the shape of the pulse waveform. We report results from preliminary studies on a range of patient populations and discuss the accuracy and limitations of this capacitive-based technology and its potential application in hospitals and communities.

Aging steepens the slope of power spectrum density of 30-minute continuous blood pressure recording in healthy human subjects

BACKGROUND

The increase of blood pressure (BP) variability (BPV) is recognized as an important additional cardiovascular risk factor in both normotensive subjects and hypertensive patients. Aging-induced atherosclerosis and autonomic dysfunction impair the baroreflex and, in turn, augment 24-hour BPV. In small and large animal experiments, impaired baroreflex steepens the slope of the power spectrum density (PSD) of continuous BP in the frequency range of 0.01 to 0.1 Hz. Although the repeated oscillometric BP recording over 24 hours or longer is a prerequisite to quantify BPV in humans, how the very short-term continuous BP recording reflects BPV remains unknown. This study aimed to evaluate the impact of aging on the very short-term (30-min) BPV in healthy human subjects by frequency analysis.

METHODS

We recorded continuous BP tonometrically for 30 min in 56 healthy subjects aged between 28 and 85 years. Considering the frequency-dependence of the baroreflex dynamic function, we estimated the PSD of BP in the frequency range of 0.01 to 0.1 Hz, and compared the characteristics of PSD among four age groups (26-40, 41-55, 56-70 and 71-85 years).

RESULTS

Aging did not significantly alter mean and standard deviation (SD) of BP among four age groups. PSD was nearly flat around 0.01 Hz and decreased gradually as the frequency increased. The slope of PSD between 0.01 and 0.1 Hz was steeper in older subjects (71 years or older) than in younger subjects (55 years or younger) (p < 0.05).

CONCLUSIONS

Aging steepened the slope of PSD of BP between 0.01 and 0.1 Hz. This phenomenon may partly be related to the deterioration of the baroreflex in older subjects. Our proposed method to evaluate very short-term continuous BP recordings may contribute to the stratification of BPV.

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.

Blood pressure measurements with the OptiBP smartphone app validated against reference auscultatory measurements

Mobile health diagnostics have been shown to be effective and scalable for chronic disease detection and management. By maximizing the smartphones' optics and computational power, they could allow assessment of physiological information from the morphology of pulse waves and thus estimate cuffless blood pressure (BP). We trained the parameters of an existing pulse wave analysis algorithm (oBPM), previously validated in anaesthesia on pulse oximeter signals, by collecting optical signals from 51 patients fingertips via a smartphone while simultaneously acquiring BP measurements through an arterial catheter. We then compared smartphone-based measurements obtained on 50 participants in an ambulatory setting via the OptiBP app against simultaneously acquired auscultatory systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean blood pressure (MBP) measurements. Patients were normotensive (70.0% for SBP versus 61.4% for DBP), hypertensive (17.1% vs. 13.6%) or hypotensive (12.9% vs. 25.0%). The difference in BP (mean ± standard deviation) between both methods were within the ISO 81,060-2:2018 standard for SBP (- 0.7 ± 7.7 mmHg), DBP (- 0.4 ± 4.5 mmHg) and MBP (- 0.6 ± 5.2 mmHg). These results demonstrate that BP can be measured with accuracy at the finger using the OptiBP smartphone app. This may become an important tool to detect hypertension in various settings, for example in low-income countries, where the availability of smartphones is high but access to health care is low.

Cuffless Blood Pressure Estimation Using Features Extracted from Carotid Dual-Diameter Waveforms

The major challenges in deep learning approaches to cuffless blood pressure estimation is selecting the most appropriate representative of the blood pulse waveform and extraction of relevant features for data collection. This paper performs an analysis of a novel dataset consisting of 71 features from the carotid dual-diameter waveforms and 4 blood pressure parameters. In particular, the analysis uses gradient boosting and graph-theoretic algorithms to determine (1) features with high predictive power and (2) potential to be pruned. Identifying such features and understanding their physiological significance is important for building blood pressure estimation models using machine learning that is robust across diverse clinical environments and patient sets.

Wearable Devices to Monitor and Reduce the Risk of Cardiovascular Disease: Evidence and Opportunities

There is a growing interest in using wearable devices to improve cardiovascular risk factors and care. This review evaluates how wearable devices are used for cardiovascular disease monitoring and risk reduction. Wearables have been evaluated for detecting arrhythmias (e.g., atrial fibrillation) as well as monitoring physical activity, sleep, and blood pressure. Thus far, most interventions for risk reduction have focused on increasing physical activity. Interventions have been more successful if the use of wearable devices is combined with an engagement strategy such as incorporating principles from behavioral economics to integrate social or financial incentives. As the technology continues to evolve, wearable devices could be an important part of remote-monitoring interventions but are more likely to be effective at improving cardiovascular care if integrated into programs that use an effective behavior change strategy.

Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study

BACKGROUND

Smartphone-based blood pressure (BP) monitoring using photoplethysmography (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension.

OBJECTIVE

This study aimed to develop a mobile personal health care system for noninvasive, pervasive, and continuous estimation of BP level and variability, which is user friendly for elderly people.

METHODS

The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless, and wearable PPG-only sensor and a native purposely designed smartphone app using multilayer perceptron machine learning techniques from raw signals. We performed a development and usability study with three older adults (mean age 61.3 years, SD 1.5 years; 66% women) to test the usability and accuracy of the smartphone-based BP monitor.

RESULTS

The employed artificial neural network model had good average accuracy (>90%) and very strong correlation (>0.90) (P<.001) for predicting the reference BP values of our validation sample (n=150). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. However, according to the Association for the Advancement of Medical Instrumentation and British Hypertension Society standards, only diastolic blood pressure prediction met the clinically accepted accuracy thresholds.

CONCLUSIONS

With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of health care, particularly in rural zones, areas lacking physicians, and areas with solitary elderly populations.

Noninvasive Blood Pressure Classification Based on Photoplethysmography Using K-Nearest Neighbors Algorithm: A Feasibility Study

Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classification accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the K-nearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPG–BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual pre-processing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.

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.