Home telemonitoring of vital signs--technical challenges and future directions

Non-Invasive Blood Pressure Sensing via Machine Learning

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

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

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

Using Biometric Technology for Telehealth and Telerehabilitation

This article discusses the use of physical and biometric sensors in telerehabilitation. It also discusses synchronous tele-physical assessment using haptics and augmented reality and asynchronous physical assessment using remote pose estimation. The article additionally focuses on computational models that have the potential to monitor and evaluate changes in kinematic and kinetic properties during telerehabilitation using biometric sensors such as electromyography and other wearable and noncontact sensors based on force and speed. And finally, the article discusses how virtual reality environments can be facilitated in telerehabilitation.

A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study

BACKGROUND

Telehealth is an effective means to assist existing health care systems, particularly for the current aging society. However, most extant telehealth systems employ individual data sources by offline data processing, which may not recognize health deterioration in a timely way.

OBJECTIVE

Our study objective was two-fold: to design and implement an integrated, personalized telehealth system on a community-based level; and to evaluate the system from the perspective of user acceptance.

METHODS

The system was designed to capture and record older adults' health-related information (eg, daily activities, continuous vital signs, and gait behaviors) through multiple measuring tools. State-of-the-art data mining techniques can be integrated to detect statistically significant changes in daily records, based on which a decision support system could emit warnings to older adults, their family members, and their caregivers for appropriate interventions to prevent further health deterioration. A total of 45 older adults recruited from 3 elderly care centers in Hong Kong were instructed to use the system for 3 months. Exploratory data analysis was conducted to summarize the collected datasets. For system evaluation, we used a customized acceptance questionnaire to examine users' attitudes, self-efficacy, perceived usefulness, perceived ease of use, and behavioral intention on the system.

RESULTS

A total of 179 follow-up sessions were conducted in the 3 elderly care centers. The results of exploratory data analysis showed some significant differences in the participants' daily records and vital signs (eg, steps, body temperature, and systolic blood pressure) among the 3 centers. The participants perceived that using the system is a good idea (ie, attitude: mean 5.67, SD 1.06), comfortable (ie, self-efficacy: mean 4.92, SD 1.11), useful to improve their health (ie, perceived usefulness: mean 4.99, SD 0.91), and easy to use (ie, perceived ease of use: mean 4.99, SD 1.00). In general, the participants showed a positive intention to use the first version of our personalized telehealth system in their future health management (ie, behavioral intention: mean 4.45, SD 1.78).

CONCLUSIONS

The proposed health monitoring system provides an example design for monitoring older adults' health status based on multiple data sources, which can help develop reliable and accurate predictive analytics. The results can serve as a guideline for researchers and stakeholders (eg, policymakers, elderly care centers, and health care providers) who provide care for older adults through such a telehealth system.

Experiences With Developing and Using Vital Sign Telemonitoring to Support Mobile Nursing in Rural Regions: Feasibility and Usability Study

Background

Modern information and communication technology has the potential to support mobile care in rural regions such as the Alpine region, which is characterized by long distances or even physically unreachable areas.

Objective

This study investigated the potential of supporting mobile nursing organizations in rural regions with the use of mobile telemonitoring systems in a case study setting.

Methods

As a subproject of the European Union–funded project INTESI, the VITAl parameter MOnitoring (VITAMO) project gathered stakeholders’ requirements for telemonitoring support of mobile care in rural regions and then developed and implemented a prototype system that was used for a 3-month test period with a local nursing organization in Austria. Log analysis, surveys, and interviews were used to evaluate the system according to the Technology Acceptance Model. The focus was technology assessment and user satisfaction of both patients and nurses.

Results

Participants were provided Bluetooth devices to measure blood pressure, body weight, and blood glucose and to track activity. They also received a tablet with a mobile internet connection to see the results. The nurses were able to access the results remotely. Regularly executed speed tests and log analysis demonstrated the availability of high-speed mobile internet in the rural test region. Log analysis, surveys, and interviews revealed the suitability of the technology environment and showed that the system was easy to use and potentially useful. The perceived usefulness for supporting mobile care was rated meaningfully low, and the frequency of nurses using the tool declined continuously over the field test period. Further group discussions investigated this issue.

Conclusions

While the technology environment with mobile internet, Bluetooth devices, and smart vital sign monitoring devices was adequate and suitable to support mobile nursing in rural regions, the potential benefit for the nursing organization could not be confirmed. Further analysis revealed that operational care processes did not follow a well-defined care strategy. Technology has the potential to leverage the available environment for developing meaningful solutions. These experiences could contribute to further investigations that need to identify and analyze existing mobile care processes at an organizational level.

Clinical Relevance of Home Monitoring of Vital Signs and Blood Glucose Levels: A Narrative Review

OBJECTIVES

We sought to assess the presence and reporting quality of peer-reviewed literature concerning the accuracy, precision, and reliability of home monitoring technologies for vital signs and glucose determinations in older adult populations.

METHODS

A narrative literature review was undertaken searching the databases Medline, Embase, and Compendex. Peer-reviewed publications with keywords related to vital signs, monitoring devices and technologies, independent living, and older adults were searched. Publications between the years 2012 and 2018 were included. Two reviewers independently conducted title and abstract screening, and four reviewers independently undertook full-text screening and data extraction with all disagreements resolved through discussion and consensus.

RESULTS

Two hundred nine articles were included. Our review showed limited assessment and low-quality reporting of evidence concerning the accuracy, precision, and reliability of home monitoring technologies. Of 209 articles describing a relevant device, only 45 percent (n = 95) provided a citation or some evidence to support their validation claim. Of forty-eight articles that described the use of a comparator device, 65 percent (n = 31) used low-quality statistical methods, 23 percent (n = 11) used moderate-quality statistical methods, and only 12 percent (n = 6) used high-quality statistical methods.

CONCLUSIONS

Our review found that current validity claims were based on low-quality assessments that do not provide the necessary confidence needed by clinicians for medical decision-making purposes. This narrative review highlights the need for standardized health technology reporting to increase health practitioner confidence in these devices, support the appropriate adoption of such devices within the healthcare system, and improve health outcomes.

A Cloud-Based Smartphone Solution for Transmitting Bio-Signals From an Emergency Response Vehicle

Most developing countries are currently unable to provide adequate, let alone advanced healthcare support to rural areas. Telemedicine combines the capability of information technology and dedicated people working towards the common goal of providing good quality healthcare in remote areas. In this article, the authors propose a system that can be used to transmit patient vitals like pulse rate, oxygen saturation, and perfusion index readings to a doctor in a remote area, while a patient is in transit. This system uses a smartphone application, a pulse oximeter, and the real-time data transferring capabilities of Firebase (a cloud database). The application has been tested under various network conditions which includes connection types such as 2G (2nd Generation), 3G (3rd Generation), 4G (4th Generation), and Fiber To The Home (FTTH). The work also discusses the possible reasons for the higher performance found in 4G networks over 3G and 2G cellular connections.

Telemonitoring to Manage Chronic Obstructive Pulmonary Disease: Systematic Literature Review

Background

Chronic obstructive pulmonary disease (COPD) is a leading cause of death throughout the world. Telemedicine has been utilized for many diseases and its prevalence is increasing in the United States. Telemonitoring of patients with COPD has the potential to help patients manage disease and predict exacerbations.

Objective

The objective of this review is to evaluate the effectiveness of telemonitoring to manage COPD. Researchers want to determine how telemonitoring has been used to observe COPD and we are hoping this will lead to more research in telemonitoring of this disease.

Methods

This review was conducted in accordance with the Assessment for Multiple Systematic Reviews (AMSTAR) and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Authors performed a systematic review of the PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases to obtain relevant articles. Articles were then accepted or rejected by group consensus. Each article was read and authors identified barriers and facilitators to effectiveness of telemonitoring of COPD.

Results

Results indicate that conflicting information exists for the effectiveness of telemonitoring of patients with COPD. Primarily, 13 out of 29 (45%) articles stated that patient outcomes were improved overall with telemonitoring, while 11 of 29 (38%) indicated no improvement. Authors identified the following facilitators: reduced need for in-person visits, better disease management, and bolstered patient-provider relationship. Important barriers included low-quality data, increased workload for providers, and cost.

Conclusions

The high variability between the articles and the ways they provided telemonitoring services created conflicting results from the literature review. Future research should emphasize standardization of telemonitoring services and predictability of exacerbations.

Monitoring data quality for telehealth systems in the presence of missing data

BACKGROUND

All-in-one station-based health monitoring devices are implemented in elder homes in Hong Kong to support the monitoring of vital signs of the elderly. During a pilot study, it was discovered that the systolic blood pressure was incorrectly measured during multiple weeks. A real-time solution was needed to identify future data quality issues as soon as possible.

METHODS

Control charts are an effective tool for real-time monitoring and signaling issues (changes) in data. In this study, as in other healthcare applications, many observations are missing. Few methods are available for monitoring data with missing observations. A data quality monitoring method is developed to signal issues with the accuracy of the collected data quickly. This method has the ability to deal with missing observations. A Hotelling's T-squared control chart is selected as the basis for our proposed method.

FINDINGS

The proposed method is retrospectively validated on a case study with a known measurement error in the systolic blood pressure measurements. The method is able to adequately detect this data quality problem. The proposed method was integrated into a personalized telehealth monitoring system and prospectively implemented in a second case study. It was found that the proposed scheme supports the control of data quality.

CONCLUSIONS

Data quality is an important issue and control charts are useful for real-time monitoring of data quality. However, these charts must be adjusted to account for missing data that often occur in healthcare context.

A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health

Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50⁻90%, coupled with a bit rate reduction by 50⁻80%, and an overall space savings in the range of 68⁻92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%.

Data to diagnosis in global health: a 3P approach

BACKGROUND

With connected medical devices fast becoming ubiquitous in healthcare monitoring there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge.

METHODS

To address this challenge, we present a 3P approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts. In a collaborative work with doctors, we present the design, development, and testing of a healthcare data analytics and communication framework that we call RASPRO (Rapid Active Summarization for effective PROgnosis). The heart of RASPRO is Physician Assist Filters (PAF) that transform unwieldy multi-sensor time series data into summarized patient/disease specific trends in steps of progressive precision as demanded by the doctor for patient's personalized condition at hand and help in identifying and subsequently predictively alerting the onset of critical conditions. The output of PAFs is a clinically useful, yet extremely succinct summary of a patient's medical condition, represented as a motif, which could be sent to remote doctors even over SMS, reducing the need for data bandwidths. We evaluate the clinical validity of these techniques using SVM machine learning models measuring both the predictive power and its ability to classify disease condition. We used more than 16,000 min of patient data (N=70) from the openly available MIMIC II database for conducting these experiments. Furthermore, we also report the clinical utility of the system through doctor feedback from a large super-speciality hospital in India.

RESULTS

The results show that the RASPRO motifs perform as well as (and in many cases better than) raw time series data. In addition, we also see improvement in diagnostic performance using optimized sensor severity threshold ranges set using the personalization PAF severity quantizer.

CONCLUSION

The RASPRO-PAF system and the associated techniques are found to be useful in many healthcare applications, especially in remote patient monitoring. The personalization, precision, and prevention PAFs presented in the paper successfully shows remarkable performance in satisfying the goals of 3Ps, thereby providing the advantages of three A's: availability, affordability, and accessibility in the global health scenario.

Using home sensing technology to assess outcome and recovery after hip and knee replacement in the UK: the HEmiSPHERE study protocol

INTRODUCTION

Over 160 000 people with severe hip or knee pain caused by osteoarthritis undergo total hip (THR) or knee replacement (TKR) surgery each year in the UK within the National Health Service (NHS), and this number is expected to increase. Innovative approaches to evaluating surgical outcomes will be needed to respond to the increasing burden of joint replacement surgery. The Sensor Platform for Healthcare in a Residential Environment, Interdisciplinary Research Collaboration (SPHERE-IRC) have developed a system of sensors that can monitor the health-related behaviours of people living at home. The system includes sensors for the home environment (measuring temperature, humidity, room occupancy, water and electricity usage), a wristband body-worn activity monitor and silhouette (body outline) sensors. The aim of HEmiSPHERE (Hip and knEe study of a Sensor Platform of HEalthcare in a Residential Environment) is to (1) determine the accuracy and feasibility of the sensory data as it compares with conventional assessment of health outcomes after surgery using patient self-reported questionnaires, and (2) to explore how the SPHERE system is useful for everyday clinical decision-making.

METHODS AND ANALYSIS

A feasibility study recruiting and installing the SPHERE system in the homes of up to 30 NHS adult patients as they undergo a THR or TKR. Through a mixed-methods design, the SPHERE system will monitor and record continuous measurements of daily behaviour. Main outcomes will assess the relationships between environmental, behavioural and movement data and the parameters of interest from the standard clinical assessments measuring patient outcomes over time. Patient interviews and focus groups with consultant orthopaedic surgeons will provide in-depth understanding of the acceptability, feasibility and accuracy of the data.

ETHICS AND DISSEMINATION

We aim to disseminate the findings through regional talks and seminars, international conferences and peer-reviewed journals and social media.

Piezoelectric extraction of ECG signal

The monitoring and early detection of abnormalities or variations in the cardiac cycle functionality are very critical practices and have significant impact on the prevention of heart diseases and their associated complications. Currently, in the field of biomedical engineering, there is a growing need for devices capable of measuring and monitoring a wide range of cardiac cycle parameters continuously, effectively and on a real-time basis using easily accessible and reusable probes. In this paper, the revolutionary generation and extraction of the corresponding ECG signal using a piezoelectric transducer as alternative for the ECG will be discussed. The piezoelectric transducer pick up the vibrations from the heart beats and convert them into electrical output signals. To this end, piezoelectric and signal processing techniques were employed to extract the ECG corresponding signal from the piezoelectric output voltage signal. The measured electrode based and the extracted piezoelectric based ECG traces are well corroborated. Their peaks amplitudes and locations are well aligned with each other.

Tele-Health Monitoring of Patient Wellness

The vital signs of chronically ill patients are monitored daily. The record flags when a specific vital sign is stable or when it trends into dangerous territory. Patients also self-assess their current state of well-being, i.e. whether they are feeling worse than usual, neither unwell nor very well compared to usual, or are feeling better than usual. This paper examines whether past vital sign data can be used to forecast how well a patient is going to feel the next day. Reliable forecasting of a chronically sick patient’s likely state of health would be useful in regulating the care provided by a community nurse, scheduling care when the patient needs it most. The hypothesis is that the vital signs indicate a trend before a person feels unwell and, therefore, are lead indicators of a patient going to feel unwell. Time series and classification or regression tree methods are used to simplify the process of observing multiple measurements such as body temperature, heart rate, etc., by selecting the vital sign measures, which best forecast well-being. We use machine learning techniques to automatically find the best combination of these vital sign measurements and their rules that forecast the wellness of individual patients. The machine learning models provide rules that can be used to monitor the future wellness of a patient and regulate their care plans.

Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review

Major reported factors associated with the limited effectiveness of home telemonitoring interventions in chronic respiratory conditions include the lack of useful early predictors, poor patient compliance and the poor performance of conventional algorithms for detecting deteriorations. This article provides a systematic review of existing algorithms and the factors associated with their performance in detecting exacerbations and supporting clinical decisions in patients with chronic obstructive pulmonary disease (COPD) or asthma. An electronic literature search in Medline, Scopus, Web of Science and Cochrane library was conducted to identify relevant articles published between 2005 and July 2015. A total of 20 studies (16 COPD, 4 asthma) that included research about the use of algorithms in telemonitoring interventions in asthma and COPD were selected. Differences on the applied definition of exacerbation, telemonitoring duration, acquired physiological signals and symptoms, type of technology deployed and algorithms used were found. Predictive models with good clinically reliability have yet to be defined, and are an important goal for the future development of telehealth in chronic respiratory conditions. New predictive models incorporating both symptoms and physiological signals are being tested in telemonitoring interventions with positive outcomes. However, the underpinning algorithms behind these models need be validated in larger samples of patients, for longer periods of time and with well-established protocols. In addition, further research is needed to identify novel predictors that enable the early detection of deteriorations, especially in COPD. Only then will telemonitoring achieve the aim of preventing hospital admissions, contributing to the reduction of health resource utilization and improving the quality of life of patients.

Automated integration of wireless biosignal collection devices for patient-centred decision-making in point-of-care systems

The proper acquisition of biosignals data from various biosensor devices and their remote accessibility are still issues that prevent the wide adoption of point-of-care systems in the routine of monitoring chronic patients. This Letter presents an advanced framework for enabling patient monitoring that utilises a cloud computing infrastructure for data management and analysis. The framework introduces also a local mechanism for uniform biosignals collection from wearables and biosignal sensors, and decision support modules, in order to enable prompt and essential decisions. A prototype smartphone application and the related cloud modules have been implemented for demonstrating the value of the proposed framework. Initial results regarding the performance of the system and the effectiveness in data management and decision-making have been quite encouraging.

Effectiveness of Telemonitoring in Patients with Chronic Obstructive Pulmonary Disease in Taiwan-A Randomized Controlled Trial

Chronic obstructive pulmonary disease (COPD) is the leading cause of death worldwide, and poses a substantial economic and social burden. Telemonitoring has been proposed as a solution to this growing problem, but its impact on patient outcome is equivocal. This randomized controlled trial aimed to investigate effectiveness of telemonitoring in improving COPD patient outcome. In total, 106 subjects were randomly assigned to the telemonitoring (n = 53) or usual care (n = 53) group. During the two months following discharge, telemonitoring group patients had to report their symptoms daily using an electronic diary. The primary outcome measure was time to first re-admission for COPD exacerbation within six months of discharge. During the follow-up period, time to first re-admission for COPD exacerbation was significantly increased in the telemonitoring group than in the usual care group (p = 0.026). Telemonitoring was also associated with a reduced number of all-cause re-admissions (0.23 vs. 0.68/patient; p = 0.002) and emergency room visits (0.36 vs. 0.91/patient; p = 0.006). In conclusion, telemonitoring intervention was associated with improved outcomes among COPD patients admitted for exacerbation in a country characterized by a small territory and high accessibility to medical services. The findings are encouraging and add further support to implementation of telemonitoring as part of COPD care.

An Open Architecture to Support Social and Health Services in a Smart TV Environment

OBJECTIVE

To design, implement, and test a solution to provide social and health services for the elderly at home based on smart TV technologies and access to all services.

METHODS

The architecture proposed is based on an open software platform and standard personal computing hardware. This provides great flexibility to develop new applications over the underlying infrastructure or to integrate new devices, for instance to monitor a broad range of vital signs in those cases where home monitoring is required.

RESULTS

An actual system as a proof-of-concept was designed, implemented, and deployed. Applications range from social network clients to vital signs monitoring; from interactive TV contests to conventional online care applications such as medication reminders or telemedicine.

CONCLUSION

In both cases, the results have been very positive, confirming the initial perception of the TV as a convenient, easy-to-use technology to provide social and health care. The TV set is a much more familiar computing interface for most senior users, and as a consequence, smart TVs become a most convenient solution for the design and implementation of applications and services targeted to this user group.

SIGNIFICANCE

This proposal has been tested in real setting with 62 senior people at their homes. Users included both individuals with experience using computers and others reluctant to them.

Non-Invasive measurement of blood pressure - Why we should look at BP traces rather than listen to Korotkoff sounds

Accurate non-invasive measurement of blood pressure in unsupervised environments continues to be a challenge, particularly in the presence of movement artefact, electrical noise and most importantly cardiac arrhythmia which are common in those aged over 65 suffering from a range of chronic conditions. Large intra personal variability in signal morphometry and amplitudes further complicates the development of reliable signal processing algorithms for NIBP measurement. In this paper we demonstrate the effect of this variability and propose that the traditional methods of human blood pressure determination by sphygmomanometry should no longer be considered a gold standard for the calibration of NIBP devices.