Smart wearable devices in cardiovascular care: where we are and how to move forward

Factors Affecting the Usage of Wearable Device Technology for Healthcare among Indian Adults: A Cross-Sectional Study

BACKGROUND

Wearable device technology has recently been involved in the healthcare industry substantially. India is the world's third largest market for wearable devices and is projected to expand at a compound annual growth rate of ~26.33%. However, there is a paucity of literature analyzing the factors determining the acceptance of wearable healthcare device technology among low-middle-income countries.

METHODS

This cross-sectional, web-based survey aims to analyze the perceptions affecting the adoption and usage of wearable devices among the Indian population aged 16 years and above.

RESULTS

A total of 495 responses were obtained. In all, 50.3% were aged between 25-50 years and 51.3% belonged to the lower-income group. While 62.2% of the participants reported using wearable devices for managing their health, 29.3% were using them daily. technology and task fitness (TTF) showed a significant positive correlation with connectivity (r = 0.716), health care (r = 0.780), communication (r = 0.637), infotainment (r = 0.598), perceived usefulness (PU) (r = 0.792), and perceived ease of use (PEOU) (r = 0.800). Behavioral intention (BI) to use wearable devices positively correlated with PEOU (r = 0.644) and PU (r = 0.711). All factors affecting the use of wearable devices studied had higher mean scores among participants who were already using wearable devices. Male respondents had significantly higher mean scores for BI (p = 0.034) and PEOU (p = 0.009). Respondents older than 25 years of age had higher mean scores for BI (p = 0.027) and Infotainment (p = 0.032).

CONCLUSIONS

This study found a significant correlation with the adoption and acceptance of wearable devices for healthcare management in the Indian context.

Biomechanical Sensing Systems for Cardiac Activity Monitoring

Cardiovascular disease is consistently ranked high among the causes of death on a global scale. Monitoring of cardiovascular signs throughout the course of a long period of time and in real time is necessary in order to discover anomalies and begin early intervention at the appropriate time. To this purpose, a significant amount of interest among researchers has been directed toward the creation of flexible sensors that may be worn or implanted and are capable of constant, immediate observation of a variety of main physiological indicators. The real-time readings of the heart and arteries' pressure fluctuations can be reflected directly by mechanical sensors, which are one of the many types of sensors. Potential benefits of mechanical sensors include excellent accuracy and considerable versatility. Capacitive, piezoresistive, piezoelectric, and triboelectric principles are the foundations of the four types of mechanical sensors that are discussed in this article as recent developments for the purpose of monitoring the cardiovascular system. The biomechanical systems that are present in the cardiovascular system are then detailed, along with their monitoring, and this includes blood and endocardial pressure, pulse wave, and heart rhythm. In conclusion, we examine the usefulness of the use of continuous health monitoring for the treatment of vascular disease and highlight the difficulties associated with its translation into clinical practice.

Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability-What We Know and What Is Coming

Heart rate at rest and exercise may predict cardiovascular risk. Heart rate variability is a measure of variation in time between each heartbeat, representing the balance between the parasympathetic and sympathetic nervous system and may predict adverse cardiovascular events. With advances in technology and increasing commercial interest, the scope of remote monitoring health systems has expanded. In this review, we discuss the concepts behind cardiac signal generation and recording, wearable devices, pros and cons focusing on accuracy, ease of application of commercial and medical grade diagnostic devices, which showed promising results in terms of reliability and value. Incorporation of artificial intelligence and cloud based remote monitoring have been evolving to facilitate timely data processing, improve patient convenience and ensure data security.

Apple Watch-guided diagnosis of AVNRT in a pregnant woman—A case report and literature review

Cardiac arrhythmias occurring during pregnancy pose a therapeutic problem as antiarrhythmic drugs can be potentially harmful to the fetus. A 35-years-old woman in the 20th week of pregnancy was admitted to the Department of Cardiology due to the first episode of arrhythmia in her life. During the event, the patient was wearing an Apple Watch Series 6, which records a 30-sec single-channel ECG. The recording showed narrow QRS complex tachycardia of 216 bpm, and short RP interval and atrioventricular nodal reentrant tachycardia (AVNRT) was recognized. Due to the mild nature of the arrhythmia, antiarrhythmic pharmacotherapy was not initiated. The use of mobile health (mHealth) devices such as wearables and health monitoring applications is now a valuable addition to routine cardiac diagnostics for patients of all ages and levels of cardiovascular risk.

Diagnosing hypertension in primary care: a retrospective cohort study to investigate the importance of night-time blood pressure assessment

BACKGROUND

Ambulatory blood-pressure monitoring (ABPM) has become less frequent in primary care since the COVID-19 pandemic, with home blood-pressure monitoring (HBPM) often the preferred alternative; however, HBPM cannot measure night-time blood pressure (BP), and patients whose night-time BP does not dip, or rises (reverse dipping), have poorer cardiovascular outcomes.

AIM

To investigate the importance of measuring night-time BP when assessing individuals for hypertension.

DESIGN AND SETTING

Retrospective cohort study of two patient populations - namely, hospital patients admitted to four UK acute hospitals located in Oxfordshire, and participants of the BP in different ethnic groups (BP-Eth) study, who were recruited from 28 UK general practices in the West Midlands.

METHOD

Using BP data collected for the two cohorts, three systolic BP phenotypes (dipper, non-dipper, and reverse dipper) were studied.

RESULTS

Among the hospital cohort, 48.9% (n = 10 610/21 716) patients were 'reverse dippers', with an average day-night systolic BP difference of +8.0 mmHg. Among the community (BP-Eth) cohort, 10.8% (n = 63/585) of patients were reverse dippers, with an average day-night systolic BP difference of +8.5 mmHg. Non-dipper and reverse-dipper phenotypes both had lower daytime systolic BP and higher night-time systolic BP than the dipper phenotype. Average daytime systolic BP was lowest in the reverse-dipping phenotype (this was 6.5 mmHg and 6.8 mmHg lower than for the dipper phenotype in the hospital and community cohorts, respectively), thereby placing them at risk of undiagnosed, or masked, hypertension.

CONCLUSION

Not measuring night-time BP puts all groups, other than dippers, at risk of failure to identify hypertension. As a result of this study, it is recommended that GPs should offer ABPM to all patients aged ≥60 years as a minimum when assessing for hypertension.

Remote digital smart device follow-up in prospective clinical trials: early insights from ORBITA-2, ORBITA-COSMIC, and ORBITA-STAR

Smart devices are a fundamental media for acquisition, processing, storage, and transfer of digital health data. The global penetration and high frequency usage of smart devices such as smartphones and fitness monitors provide us an opportunity for incorporation into clinical trials to generate more clinically meaningful data. Reporting of angina can significantly vary between patients and also within patients at different timepoints. Furthermore, the nature of angina can lead to variation in ways patients adapt their activities of daily living and hence reporting of symptoms and quality of life. Current clinical trials investigating the effects of intervention on angina do not accurately incorporate these patient centred outcomes and considerations. Hence, methods to contemporaneously assess daily angina burden in a convenient, patient focused, and cost-effective manner are priorities for contemporary clinical trials to address. In this article, we provide our insights into the use of remote digital smart devices in clinical trials of stable coronary artery disease conducted by our research group. We discuss how our experiences from previous trials necessitated its incorporation and will provide us with important data that will inform clinical practice. We discuss the benefits and current challenges and limitations of smart device incorporation while providing our procedural workflow for how we incorporated smart devices into our clinical trials for others to consider. We hope that this approach will allow us to understand the perceptions and implications of angina on patient lives with greater granularity than previously explored.

Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.

Definition of a Method for the Evaluation of Telemedicine Platforms in the Italian Context

Background: The COVID-19 outbreak led to the diffusion of several telemedicine solutions. The choice of the correct platform is crucial for ensuring the release of effective assistance. However, there is a lack of an objective method for the assessment of technical features. Objective: This study proposes a methodology for the evaluation of functional requirements of telemedicine platforms. This approach also permits the comparison of solutions in the Italian market by means of defined parameters, thus directing the choice of health care professionals. Methods: The study is divided into three phases. First, a mapping of the telemedicine platforms operating in Italy is performed. Then, the available platforms are selected based on the offered telemedicine activity. Finally, a method for evaluating the investigated platforms is defined. Results: Thirty-three (n = 33) technological systems were identified through an accurate investigation on the web and interviews with IT companies. Fifteen parameters were defined and organized into three categories: (1) usability of the telemedicine platform, (2) security, and (3) technological and organizational aspects. A score between 1 and 4 was assigned to each parameter, proportionally to the completeness of the platform. In particular, 62.96% of platforms reached an average score between 3.01 and 4 points; 33.33% of them had scores between 2.01 and 3, while the remaining 3.70% of solutions obtained a result between 1.01 and 2. Conclusions: The study provides an evaluation approach that is easily usable by health professionals to select the most suitable platform. The number of solutions and quality of information could be updated to obtain a complete tool.

Factors affecting resting heart rate in free-living healthy humans

Resting heart rate (RHR) is a potential cardiac disease prevention target because it is strongly associated with cardiac morbidity and mortality, yet community-based monitoring of RHR remains in its infancy. Recently, smartwatches have become available enabling measurement with non-intrusive devices of relationships between RHR and other factors outside the laboratory. We carried out cross-sectional observational retrospective analysis of anonymised smartwatch data obtained by participants in their everyday lives between 2016 and 2021 in a single centre community-based study, using convenience sampling. Between participants, overall RHR means strongly or moderately inversely correlated with means of stand hour (SH), calculated VO₂ max, walking and running distance (WRD), steps and flights climbed (FC). Within participants, in quarterly averages, RHR inversely correlated moderately with frequency of standing (stand hours, SH). RHR also inversely correlated moderately with heart rate variability (HRV), consistent with the known impact of increasing parasympathetic dominance on RHR. These within participant correlations suggest that RHR might be modifiable by changes in SH and HRV within individuals. Indeed, analysing paired daily data, relationships between these three categories were dose dependent. 15 SH versus 5 SH associated with a reduction of 10 beats per minute in mean RHR and increase in mean HRV of 14 ms, respectively. We conclude that within individuals, RHR inversely correlates with frequency of standing and HRV, with paired daily measurements indicating effects are mediated that day. RHR also inversely correlates with fitness and activity measures between participants. Our findings provide initial community-based observational evidence supporting further prospective interventional investigation of frequency of standing or HRV modifiers, alongside more familiar interventions, for cardiac disease prevention.

Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review

Heart rate time series are widely used to characterize physiological states and athletic performance. Among the main indicators of metabolic and physiological states, the detection of metabolic thresholds is an important tool in establishing training protocols in both sport and clinical fields. This paper reviews the most common methods, applied to heart rate (HR) time series, aiming to detect metabolic thresholds. These methodologies have been largely used to assess energy metabolism and to identify the appropriate intensity of physical exercise which can reduce body weight and improve physical fitness. Specifically, we focused on the main nonlinear signal evaluation methods using HR to identify metabolic thresholds with the purpose of identifying a method which can represent a useful tool for the real-time settings of wearable devices in sport activities. While the advantages and disadvantages of each method, and the possible applications, are presented, this review confirms that the nonlinear analysis of HR time series represents a solid, robust and noninvasive approach to assess metabolic thresholds.

Wearable, wireless, multi-sensor device for monitoring tissue circulation after free-tissue transplantation: a multicentre clinical trial

Wearable sensors have seen remarkable recent technological developments, and their role in healthcare is expected to expand. Specifically, monitoring tissue circulation in patients who have undergone reconstructive surgery is critical because blood flow deficiencies must be rescued within hours or the transplant will fail due to thrombosis/haematoma within the artery or vein. We design a wearable, wireless, continuous, multipoint sensor to monitor tissue circulation. The system measures pulse waves, skin colour, and tissue temperature to reproduce physician assessment. Data are analysed in real time for patient risk using an algorithm. This multicentre clinical trial involved 73 patients who underwent transplant surgery and had their tissue circulation monitored until postoperative day 7. Herein, we show that the overall agreement rate between physician and sensor findings is 99.2%. In addition, the patient questionnaire results indicate that the device is easy to wear. The sensor demonstrates non-invasive, real-time, continuous, multi-point, wireless, and reliable monitoring for postoperative care. This wearable system can improve the success rate of reconstructive surgeries.

The paradox of the artificial intelligence system development process: the use case of corporate wellness programs using smart wearables

Artificial intelligence (AI) systems have been widely applied to various contexts, including high-stake decision processes in healthcare, banking, and judicial systems. Some developed AI models fail to offer a fair output for specific minority groups, sparking comprehensive discussions about AI fairness. We argue that the development of AI systems is marked by a central paradox: the less participation one stakeholder has within the AI system's life cycle, the more influence they have over the way the system will function. This means that the impact on the fairness of the system is in the hands of those who are less impacted by it. However, most of the existing works ignore how different aspects of AI fairness are dynamically and adaptively affected by different stages of AI system development. To this end, we present a use case to discuss fairness in the development of corporate wellness programs using smart wearables and AI algorithms to analyze data. The four key stakeholders throughout this type of AI system development process are presented. These stakeholders are called service designer, algorithm designer, system deployer, and end-user. We identify three core aspects of AI fairness, namely, contextual fairness, model fairness, and device fairness. We propose a relative contribution of the four stakeholders to the three aspects of fairness. Furthermore, we propose the boundaries and interactions between the four roles, from which we make our conclusion about the possible unfairness in such an AI developing process.

Effectiveness of Remote PPG Construction Methods: A Preliminary Analysis

The contactless recording of a photoplethysmography (PPG) signal with a Red-Green-Blue (RGB) camera is known as remote photoplethysmography (rPPG). Studies have reported on the positive impact of using this technique, particularly in heart rate estimation, which has led to increased research on this topic among scientists. Therefore, converting from RGB signals to constructing an rPPG signal is an important step. Eight rPPG methods (plant-orthogonal-to-skin (POS), local group invariance (LGI), the chrominance-based method (CHROM), orthogonal matrix image transformation (OMIT), GREEN, independent component analysis (ICA), principal component analysis (PCA), and blood volume pulse (PBV) methods) were assessed using dynamic time warping, power spectrum analysis, and Pearson’s correlation coefficient, with different activities (at rest, during exercising in the gym, during talking, and while head rotating) and four regions of interest (ROI): the forehead, the left cheek, the right cheek, and a combination of all three ROIs. The best performing rPPG methods in all categories were the POS, LGI, and OMI methods; each performed well in all activities. Recommendations for future work are provided.

The Role of Contemporary Wearable and Handheld Devices in the Diagnosis and Management of Cardiac Arrhythmias

Cardiac arrhythmias are associated with significant morbidity, mortality and economic burden on the health care system. Detection and surveillance of cardiac arrhythmias using medical grade non-invasive methods (electrocardiogram, Holter monitoring) is the accepted standard of care. Whilst their accuracy is excellent, significant limitations remain in terms of accessibility, ease of use, cost, and a suboptimal diagnostic yield (up to ∼50%) which is critically dependent on the duration of monitoring. Contemporary wearable and handheld devices that utilise photoplethysmography and the electrocardiogram present a novel opportunity for remote screening and diagnosis of arrhythmias. They have significant advantages in terms of accessibility and availability with the potential of enhancing the diagnostic yield of episodic arrhythmias. However, there is limited data on the accuracy and diagnostic utility of these devices and their role in therapeutic decision making in clinical practice remains unclear. Evidence is mounting that they may be useful in screening for atrial fibrillation, and anecdotally, for the diagnosis of other brady and tachyarrhythmias. Recently, there has been an explosion of patient uptake of such devices for self-monitoring of arrhythmias. Frequently, the clinician is presented such information for review and comment, which may influence clinical decisions about treatment. Further studies are needed before incorporation of such technologies in routine clinical practice, given the lack of systematic data on their accuracy and utility. Moreover, challenges with regulation of quality standards and privacy remain. This state-of-the-art review summarises the role of novel ambulatory, commercially available, heart rhythm monitors in the diagnosis and management of cardiac arrhythmias and their expanding role in the diagnostic and therapeutic paradigm in cardiology.

Wearables in Cardiovascular Disease

Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.

Wearable technology for early detection of COVID-19: A systematic scoping review

Wearable technology is an emerging method for the early detection of coronavirus disease 2019 (COVID-19) infection. This scoping review explored the types, mechanisms, and accuracy of wearable technology for the early detection of COVID-19. This review was conducted according to the five-step framework of Arksey and O'Malley. Studies published between December 31, 2019 and December 15, 2021 were obtained from 10 electronic databases, namely, PubMed, Embase, Cochrane, CINAHL, PsycINFO, ProQuest, Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online. Grey literature, reference lists, and key journals were also searched. All types of articles describing wearable technology for the detection of COVID-19 infection were included. Two reviewers independently screened the articles against the eligibility criteria and extracted the data using a data charting form. A total of 40 articles were included in this review. There are 22 different types of wearable technology used to detect COVID-19 infections early in the existing literature and are categorized as smartwatches or fitness trackers (67%), medical devices (27%), or others (6%). Based on deviations in physiological characteristics, anomaly detection models that can detect COVID-19 infection early were built using artificial intelligence or statistical analysis techniques. Reported area-under-the-curve values ranged from 75% to 94.4%, and sensitivity and specificity values ranged from 36.5% to 100% and 73% to 95.3%, respectively. Further research is necessary to validate the effectiveness and clinical dependability of wearable technology before healthcare policymakers can mandate its use for remote surveillance.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

Recent trends in telerehabilitation of stroke patients: A narrative review

BACKGROUND: Stroke is the main reason for disabilities worldwide leading to motor dysfunction, spatial neglect and cognitive problems, aphasia, and other speech-language pathologies, reducing the life quality. To overcome disabilities, telerehabilitation (TR) has been recently introduced. OBJECTIVE: The aim of this review was to analyze current TR approaches for stroke patients’ recovery. METHODS: We searched 6 online databases from January 2018 to October 2021, and included 70 research and review papers in the review. We analyzed TR of 995 individuals, which was delivered synchronously and asynchronously. RESULTS: Findings show TR is feasible improving motor function, cognition, speech, and language communication among stroke patients. However, the dose of TR sessions varied significantly. We identified the following limitations: lack of equipment, software, and space for home-based exercises, insufficient internet capacity and speed, unavailability to provide hands on guidance, low digital proficiency and education, high cognitive demand, small samples, data heterogeneity, and no economic evaluation. CONCLUSIONS: The review shows TR is superior or similar to conventional rehabilitation in clinical outcomes and is used as complementary therapy or as alternative treatments. More importantly, TR provides access to rehabilitation services of a large number of patients with immobility, living in remote areas, and during COVID-19 pandemic or similar events.

Monitoring Breathing and Heart Rate Using Episodic Broadcast Data Transmission

The paper presents a wearable sensor for breath and pulse monitoring using an inertial sensor and episodic broadcast radio transmission. The data transmission control algorithm applied allows for the transmission of additional information using the standard PDU format and, at the same time, goes beyond the Bluetooth teletransmission standard (BLE). The episodic broadcast transmission makes it possible to receive information from sensors without the need to create a dedicated radio link or a defined network structure. The radio transmission controlled by the occurrence of a specific event in the monitored signal is combined with the reference wire transmission. The signals from two different types of sensors and the simulated ECG signal are used to control the BLE transmission. The presented results of laboratory tests indicate the effectiveness of episodic data transmission in the BLE standard. The conducted analysis showed that the mean difference in pulse detection using the episodic transmission compared to the wire transmission is 0.038 s, which is about 4% of the mean duration of a single cycle, assuming that the average adult human pulse is 60 BPM.

High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study

BACKGROUND

Consumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized.

OBJECTIVE

We aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk.

METHODS

We introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events.

RESULTS

We found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P<.001 in each case). Furthermore, heart rate dynamics from different activity states contain distinct information (maximum absolute correlation coefficient of 0.15). Heart rate dynamics in sedentary states are most predictive of lipid abnormalities and obesity, whereas patterns in active states are most predictive of blood pressure abnormalities (P<.001). Moreover, in comparison with standard measures, higher resolution patterns in wearable heart rate recordings are better able to represent subtle physiological dynamics related to genomic risk for cardiometabolic disease (improvement of 11.9%-22.0% in Brier scores; P<.001). Finally, illustrative case studies reveal connections between these high-resolution phenotypes and actualized clinical events, even for borderline profiles lacking apparent cardiometabolic risk markers.

CONCLUSIONS

High-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management.

[General practitioners' perception of integrated care using communication technologies: a secondary analysis of a focus group of the Horizon 2020 funded project POLYCARE]

BACKGROUND

Integrated healthcare models (IC) have the objective of reducing the costs of an increased use of health service as well as the strong sectoral fragmentation of the German health care system. However, no national approach has been implemented in primary care to date.

METHOD

Ten GPs from the Cologne/Bonn district (60% male; Ø age = 52 years [35-65]) were invited to a focus group in 2016. The interview was part of the Horizon 2020 funded POLYCARE study. A semi-structured guideline was used to assess feasibility of the POLYCARE study protocol. GPs also provided information about previous experience with and attitudes toward IC models and the relevant information and communication technologies (ICT), such as home-monitoring or communication software. This information was analyzed using a transcending secondary analysis to evaluate conditions for their successful implementation.

RESULTS

Participants reported little experience with IC and ICT. However, they reported being open to both and seeing potential for time savings, better networking opportunities, and increased quality of care for their patients. The integration of social services was considered as a chance of alleviating the burden of socio-medical tasks. Barriers to the introduction of IC and ICT were seen in the initial time investment, the lack of legal structures, the concern about overabundant data, and the susceptibility to failure.

CONCLUSION

The nationwide expansion of social services as well as ICT that is easy to use, less susceptible to failure, and compatible with existing structures show great potential for relieving GPs. Future research should address the concerns - such as financial and time expenses of introducing IC and ICT - of GPs by systematic investigation in long-term studies. The provision of an additional legal basis that is regulating the respective remuneration models as well as the rights and obligations of all parties, IC and ICT can play a greater role in future patient care.

Human activity recognition from textile electrocardiograms

Textile sensors for physiological signals bear the potential of unobtrusive and continuous application in daily life. Recently, textile electrocardiography (ECG) sensors became available which are of particular interest for physical activity monitoring due to the high effect of exercise on the heart rate. In this work, we evaluate the effectiveness of a single-lead ECG signal acquired using a non-medical-grade ECG shirt for human activity recognition (HAR). Healthy volunteers (N=10) wore the shirt during four different activities (sleeping, sitting, walking, running) in an uncontrolled environment and ECG data (256 Hz, 12 Bit) was stored, manually checked, and unusable segments (e.g. no sensor contact) were removed, resulting in a total of 228 hours of recording. Signals were split in short segments of different duration (10, 30, 60s), transformed using the Short-time Fourier Transform (STFT) to a spectrogram image and fed into a state-of-the-art convolutional neural network (CNN). The best configuration results in an F'l-Score of 73% and an accuracy of 77% on the test set. Results with leave-one-subject-out cross-validation show F'l-Scores ranging from 41 % to 80%. Thus, a single-lead, wearable-generated ECG has an informative value for HAR to a certain extent. In future work, we aim at using more sensors of the smart shirt and sensor fusion.

Upcycling Compact Discs for Flexible and Stretchable Bioelectronic Applications

Electronic waste is a global issue brought about by the short lifespan of electronics. Viable methods to relieve the inundated disposal system by repurposing the enormous amount of electronic waste remain elusive. Inspired by the need for sustainable solutions, this study resulted in a multifaceted approach to upcycling compact discs. The once-ubiquitous plates can be transformed into stretchable and flexible biosensors. Our experiments and advanced prototypes show that effective, innovative biosensors can be developed at a low-cost. An affordable craft-based mechanical cutter allows pre-determined patterns to be scored on the recycled metal, an essential first step for producing stretchable, wearable electronics. The active metal harvested from the compact discs was inert, cytocompatible, and capable of vital biopotential measurements. Additional studies examined the material’s resistive emittance, temperature sensing, real-time metabolite monitoring performance, and moisture-triggered transience. This sustainable approach for upcycling electronic waste provides an advantageous research-based waste stream that does not require cutting-edge microfabrication facilities, expensive materials, and high-caliber engineering skills.

Electronic waste is a global issue brought about by the short lifespan of electronics. Here, the authors report a process to upcycle compact discs into flexible and stretchable bio-electronics.

Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study

Ventricular arrhythmia (VA) is a leading cause of sudden death and health deterioration. Recent advances in predictive analytics and wearable technology for behavior assessment show promise but require further investigation. Yet, previous studies have only assessed other health outcomes and monitored patients for short durations (7−14 days). This study explores how behaviors reported by a consumer wearable can assist VA risk prediction. An exploratory observational study was conducted with participants who had an implantable cardioverter-defibrillator (ICD) and wore a Fitbit Alta HR consumer wearable. Fitbit reported behavioral markers for physical activity (light, fair, vigorous), sleep, and heart rate. A case-crossover analysis using conditional logistic regression assessed the effects of time-adjusted behaviors over 1−8 weeks on VA incidence. Twenty-seven patients (25 males, median age 59 years) were included. Among the participants, ICDs recorded 262 VA events during 8093 days monitored by Fitbit (median follow-up period 960 days). Longer light to fair activity durations and a higher heart rate increased the odds of a VA event (p < 0.001). In contrast, lengthier fair to vigorous activity and sleep durations decreased the odds of a VA event (p < 0.001). Future studies using consumer wearables in a larger population should prioritize these outcomes to further assess VA risk.

Recent progress in electrospun nanomaterials for wearables

Wearables have garnered significant attention in recent years not only as consumer electronics for entertainment, communications, and commerce but also for real-time continuous health monitoring. This has been spurred by advances in flexible sensors, transistors, energy storage, and harvesting devices to replace the traditional, bulky, and rigid electronic devices. However, engineering smart wearables that can seamlessly integrate with the human body is a daunting task. Some of the key material attributes that are challenging to meet are skin conformability, breathability, and biocompatibility while providing tunability of its mechanical, electrical, and chemical properties. Electrospinning has emerged as a versatile platform that can potentially address these challenges by fabricating nanofibers with tunable properties from a polymer base. In this article, we review advances in wearable electronic devices and systems that are developed using electrospinning. We cover various applications in multiple fields including healthcare, biomedicine, and energy. We review the ability to tune the electrical, physiochemical, and mechanical properties of the nanofibers underlying these applications and illustrate strategies that enable integration of these nanofibers with human skin.

Comparison of 2 Smart Watch Algorithms for Detection of Atrial Fibrillation and the Benefit of Clinician Interpretation: SMART WARS Study

BACKGROUND

Smart watches and wearable technology capable of heart rhythm assessment have increased in use in the general population. The Apple Watch Series 4 (AW4) and KardiaBand (KB) are devices capable of obtaining single-lead electrocardiographic recordings, presenting a novel opportunity for the detection of paroxysmal arrhythmias.

OBJECTIVES

The aim of this study was to assess the diagnostic utility of the AW4 and KB in an elderly outpatient population.

METHODS

Consecutive recordings were taken from patients attending cardiology outpatient clinic from the AW4 and KB concurrently with 12-lead electrocardiography. Automated diagnoses and blinded single-lead electrocardiographic tracing interpretations by 2 cardiologists were analyzed. Analysis was also conducted to assess the effect of combined device and clinician interpretation.

RESULTS

One hundred twenty-five patients were prospectively recruited (mean age 76 ± 7 years, 62% men). The accuracy of the automated rhythm assessment was higher with the KB than the AW4 (74% vs 65%). For the detection of atrial fibrillation, the sensitivity and negative predictive value of the KB were 89% and 97%, respectively, and of the AW4 were 19% and 82%, respectively. Using hybrid automated and clinician interpretation, the overall accuracy of the KB and AW4 was 91% and 87%, respectively.

CONCLUSIONS

The KB automated algorithm outperformed the AW4 in its accuracy and sensitivity for detecting atrial fibrillation in the outpatient setting. Clinician assessment of the single-lead electrocardiogram improved accuracy. These findings suggest that although these devices' tracings are of sufficient quality, automated diagnosis alone is not sufficient for making clinical decisions about atrial fibrillation diagnosis and management.

Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: Proceedings of the European Society of Cardiology: Cardiovascular Round Table

Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.

Remote control of the heart and beyond

A resorbable closed-loop sensor-actuator implant can temporarily control heart rate.

Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review

Cardiovascular diseases (CVD) are the leading cause of mortality globally. Despite improvement in therapies, people with CVD lack support for monitoring and managing their condition at home and out of hospital settings. Smart Home Technologies have potential to monitor health status and support people with CVD in their homes. We explored the Smart Home Technologies available for CVD monitoring and management in people with CVD and acceptance of the available technologies to end-users. We systematically searched four databases, namely Medline, Web of Science, Embase, and IEEE, from 1990 to 2020 (search date 18 March 2020). “Smart-Home” was defined as a system using integrated sensor technologies. We included studies using sensors, such as wearable and non-wearable devices, to capture vital signs relevant to CVD at home settings and to transfer the data using communication systems, including the gateway. We categorised the articles for parameters monitored, communication systems and data sharing, end-user applications, regulations, and user acceptance. The initial search yielded 2462 articles, and the elimination of duplicates resulted in 1760 articles. Of the 36 articles eligible for full-text screening, we selected five Smart Home Technology studies for CVD management with sensor devices connected to a gateway and having a web-based user interface. We observed that the participants of all the studies were people with heart failure. A total of three main categories—Smart Home Technology for CVD management, user acceptance, and the role of regulatory agencies—were developed and discussed. There is an imperative need to monitor CVD patients’ vital parameters regularly. However, limited Smart Home Technology is available to address CVD patients’ needs and monitor health risks. Our review suggests the need to develop and test Smart Home Technology for people with CVD. Our findings provide insights and guidelines into critical issues, including Smart Home Technology for CVD management, user acceptance, and regulatory agency’s role to be followed when designing, developing, and deploying Smart Home Technology for CVD.

Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data

Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and mortality. Despite its popularity in consumer devices, a lack of methodological standards and clinical validation remains a major impediment to step count being accepted as a valid clinical endpoint. Previous works have mainly focused on device-specific step-count algorithms and often employ sensor modalities that may not be widely available. This may limit step-count suitability in clinical scenarios. In this paper, we trained neural network models on publicly available data and tested on an independent cohort using two approaches: generalization and personalization. Specifically, we trained neural networks on accelerometer signals from one device and either directly applied them or adapted them individually to accelerometer data obtained from a separate subject cohort wearing multiple distinct devices. The best models exhibited highly accurate step-count estimates for both the generalization (96-99%) and personalization (98-99%) approaches. The results demonstrate that it is possible to develop device-agnostic, accelerometer-only algorithms that provide highly accurate step counts, positioning step count as a reliable mobility endpoint and a strong candidate for clinical validation.

Comparison of neural basis expansion analysis for interpretable time series (N-BEATS) and recurrent neural networks for heart dysfunction classification

OBJECTIVE

The primary purpose of this work is to analyze the ability of N-BEATS architecture for the problem of prediction and classification of electrocar- diogram (ECG) signals. To achieve this, performance comparison with various types of other SotA (state-of-the-art) recurrent neural network architectures commonly used for such problems is conducted.

APPROACH

Four architectures (N-BEATS, LSTM, LSTM with peepholes, GRU) were tested for performance and dimension reduction problems for different number of leads (2, 3, 4, 6, 12), both in variants consisting of blended branches, allowing retaining ac- curacy while reducing the computational capacity needed. The analysis was performed on datasets and using metrics from Challenges in Cardiology (CinC) 2021 competition.

MAIN RESULTS

Best results were achieved for LSTM with peepholes, then LSTM, GRU and the worst for N-BEATS (challenge metrics respectively: 0.42, 0.40, 0.39, 0.35; for times: 0.0395 s, 0.0036 s, 0.0027 s, 0.0002 s). Commonly used LSTM outperforms N- BEATS in terms of multi-label classification, data set resilience, and obtained challenge metrics. Still, N-BEATS can obtain acceptable results for 2 lead classification (metric of 0.35 for N-BEATS and 0.38 for other networks) and outperforms other solutions in terms of complexity and speed.

SIGNIFICANCE

This paper features a novel approach of using the N-BEATS, which was previously used only for forecasting, to classify ECG signals with success. While N- BEATS multi-label classification capacity is lower than LSTM, its speed obtaining results with a reduced number of leads (faster by one to two degrees of magnitude) allows for arrhythmias detection and classification while using off-the-shelf wearable devices (Holter monitors, sport bands, etc.).

DeepCNAP: A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography

Arterial blood pressure (ABP) monitoring may permit the early diagnosis and management of cardiovascular disease (CVD); however, existing methods for measuring ABP outside the clinic use inconvenient cuff sphygmomanometry, or do not estimate continuous ABP waveforms. This study proposes a novel deep learning model DeepCNAP for estimating continuous BP waveform from a noninvasively measured photoplethysmography (PPG) signal in real time. DeepCNAP was designed through the combination of deep convolutional networks and self-attention. The proposed method was constructed via 10-fold cross-validation based on the MIMIC database (the number of subjects = 942, recording time = 374.43 hours). The performance of DeepCNAP was evaluated from two perspectives: estimating ABP from PPG and classifying hemodynamically unstable events (i.e., hypertension, prehypertension, hypotension, and the normal state). The mean absolute errors of the BP estimates were 3.40 4.36 mmHg for systolic BP, 1.75 2.25 mmHg for diastolic BP, and 3.23 2.21 mmHg for the BP waveform, indicating that DeepCNAP satisfies the standards of both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). From the estimated BP, hypertension, prehypertension, hypotension, and the normal state were classified with 99.44, 97.58, 92.23, and 94.64% accuracy, respectively. DeepCNAP enables feasible real-time estimation of invasively measured ABP from noninvasive PPG. With its noninvasive nature, high accuracy, and clinical relevance, the proposed model could be valuable in general wards at hospitals and for wearable devices in daily life.

Wearable Devices for Physical Monitoring of Heart: A Review

Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients' biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient's medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient's health.

Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension

Pulmonary hypertension is a complex disease with multiple causes, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of pulmonary hypertension pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular -omics profiling, and other modalities. In this review, we summarize key data sets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and subphenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.

Effect of temporal resolution on the detection of cardiac arrhythmias using HRV features and machine learning

Objective.Arrhythmia is an abnormal cardiac rhythm that affects the pattern and rate of the heartbeat. Wearable devices with the functionality to measure and store heart rate (HR) data are growing in popularity and enable diagnosing and monitoring arrhythmia on a large scale. The typical sampling resolution of HR data available from non-medical grade wearable devices varies from seconds to several minutes depending on the device and its settings. However, the impact of sampling resolution on the performance and quality of arrhythmia detection has not yet been quantified.Approach.In this study, we investigated the detection and classification of three arrhythmias, namely atrial fibrillation, bradycardia, tachycardia, from down-sampled HR data with various temporal resolution (5-, 15-, 30- and 60 s averages) in 1 h segments extracted from an annotated Holter ECG database acquired at the University of Virginia Heart Station. For the classification task, a total of 15 common heart rate variability (HRV) features were engineered based on the HR time series of each patient. Three different types of machine learning classifiers were evaluated, namely logistic regression, support vector machine and random forest.Main results.A decrease in temporal resolution drastically impacted the detection of atrial fibrillation but did not substantially affect the detection of bradycardia and tachycardia. A HR resolution up to 15 s average demonstrated reasonable performance with a sensitivity of 0.92 and a specificity of 0.86 for a multiclass random forest classifier.Significance.HRV features extracted from low resolution long HR recordings have the potential to increase the early detection of arrhythmias in undiagnosed individuals.

High Specificity Wearable Device With Photoplethysmography and Six-Lead Electrocardiography for Atrial Fibrillation Detection Challenged by Frequent Premature Contractions: DoubleCheck-AF

Background

Consumer smartwatches have gained attention as mobile health (mHealth) tools able to detect atrial fibrillation (AF) using photoplethysmography (PPG) or a short strip of electrocardiogram (ECG). PPG has limited accuracy due to the movement artifacts, whereas ECG cannot be used continuously, is usually displayed as a single-lead signal and is limited in asymptomatic cases.

Objective

DoubleCheck-AF is a validation study of a wrist-worn device dedicated to providing both continuous PPG-based rhythm monitoring and instant 6-lead ECG with no wires. We evaluated its ability to differentiate between AF and sinus rhythm (SR) with particular emphasis on the challenge of frequent premature beats.

Methods and Results

We performed a prospective, non-randomized study of 344 participants including 121 patients in AF. To challenge the specificity of the device two control groups were selected: 95 patients in stable SR and 128 patients in SR with frequent premature ventricular or atrial contractions (PVCs/PACs). All ECG tracings were labeled by two independent diagnosis-blinded cardiologists as “AF,” “SR” or “Cannot be concluded.” In case of disagreement, a third cardiologist was consulted. A simultaneously recorded ECG of Holter monitor served as a reference. It revealed a high burden of ectopy in the corresponding control group: 6.2 PVCs/PACs per minute, bigeminy/trigeminy episodes in 24.2% (31/128) and runs of ≥3 beats in 9.4% (12/128) of patients. AF detection with PPG-based algorithm, ECG of the wearable and combination of both yielded sensitivity and specificity of 94.2 and 96.9%; 99.2 and 99.1%; 94.2 and 99.6%, respectively. All seven false-positive PPG-based cases were from the frequent PVCs/PACs group compared to none from the stable SR group (P < 0.001). In the majority of these cases (6/7) cardiologists were able to correct the diagnosis to SR with the help of the ECG of the device (P = 0.012).

Conclusions

This is the first wearable combining PPG-based AF detection algorithm for screening of AF together with an instant 6-lead ECG with no wires for manual rhythm confirmation. The system maintained high specificity despite a remarkable amount of frequent single or multiple premature contractions.

Global Aging and Cancer: Advancing Care Through Innovation

The oncology field, like many others, is digitalizing rapidly, a phenomenon that may have been accelerated by the COVID-19 pandemic. This movement is creating opportunities and challenges. Another rapidly developing change is the aging of the global population; because cancer is a disease of aging, there is a need for health systems to adapt to taking care of such patients. In this article, we address how these innovative technologies can be leveraged to improve the care of older patients with cancer beyond academic centers, such as in underserved areas and low- and middle-income countries. We review how digital technologies can be used to enhance the follow-up of patients in low- and middle-income countries. We also tackle the issue of training a global workforce to treat cancer in an aging population and how to leverage innovations in this matter. Finally, we review opportunities to expand the usefulness of big data and machine learning beyond academic centers to support private practices and underserved areas.

Practice Patterns of Operators Participating in the Real-World Experience of Catheter Ablation for Treatment of Symptomatic Paroxysmal and Persistent Atrial Fibrillation (REAL-AF) Registry

BACKGROUND

The Real-World Experience of Catheter Ablation for Treatment of Symptomatic Paroxysmal and Persistent Atrial Fibrillation (REAL-AF) is a multicenter prospective registry of atrial fibrillation (AF) ablation. We sought to describe the baseline workflows of REAL-AF operators.

METHODS

REAL-AF enrolls high volume minimum fluoroscopy radiofrequency ablators. A 150 item questionnaire was administered to participating operators. Responses were analyzed using standard methods.

RESULTS

Forty-two respondents had a mean 178.2 ± 89.2 yearly AF ablations, with 42.4 ± 11.9% being paroxysmal (PAF). Most operators performed ablation with uninterrupted or minimally interrupted anticoagulation (66.7% and 28.6%). Left atrial appendage (LAA) thrombus was most commonly ruled out with transesophageal echocardiography (33.3% and 42.9% for PAF and persistent AF). Consistent with registry design, radiofrequency energy (92.1% ± 18.8% of cases) and zero fluoroscopy ablation (73.8% goal 0 fluoroscopy) were common. The majority of operators relied on index-guided ablation (90.5%); Mean Visitag surpoint targets were higher anteriorly vs posteriorly (508.3 ± 49.8 vs 392.3 ± 37.0, p < 0.01), but power was similar. There was considerable heterogeneity related to gaps in current knowledge, such as lesion delivery targets and sites of extra-pulmonary vein ablation (most common was the posterior wall followed by the roof). Peri-procedural risk factor management of obesity, hypertension, and sleep apnea was common. There was a mean of 3.0 ± 1.2 follow-up visits at 12 months.

CONCLUSIONS

REAL-AF operators were high volume low fluoroscopy "real world" operators with good follow-up and adherence to known best-practices. There was disagreement related to knowledge gaps in guidelines.

Field-Based Assessments of Behavioral Patterns During Shiftwork in Police Academy Trainees Using Wearable Technology

Circadian misalignment, as occurs in shiftwork, is associated with numerous negative health outcomes. Here, we sought to improve data labeling accuracy from wearable technology using a novel data pre-processing algorithm in 27 police trainees during shiftwork. Secondarily, we explored changes in four metabolic salivary biomarkers of circadian rhythm during shiftwork. Using a two-group observational study design, participants completed in-class training during dayshift for 6 weeks followed by either dayshift or nightshift field-training for 6 weeks. Using our novel algorithm, we imputed labels of circadian misaligned sleep episodes that occurred during daytime, which were previously were mislabeled as non-sleep by Garmin, supported by algorithm performance analysis. We next assessed changes to resting heart rate and sleep regularity index during dayshift versus nightshift field-training. We also examined changes in field-based assessments of salivary cortisol, uric acid, testosterone, and melatonin during dayshift versus nightshift. Compared to dayshift, nightshift workers experienced larger changes to resting heart rate, sleep regularity index (indicating reduced sleep regularity), and alterations in sleep/wake activity patterns accompanied by blunted salivary cortisol. Salivary uric acid and testosterone did not change. These findings show wearable technology combined with specialized data pre-processing can be used to monitor changes in behavioral patterns during shiftwork.

A Disease Pathway Framework for Pain Point Identification and Elaboration of Product Requirements Across Patient Care Plan Using Innovation Think Tank Global Infrastructure

Healthcare providers as well as medical technologists lay a strong focus on clinical conditions for patient centric care delivery. Currently, the challenges are to (1) obtain a consolidated view of various stakeholders and pain points for the entire disease lifecycle, (2) identify interdependencies between different stages of the disease, and (3) prioritize solutions based on customer needs. A structured approach is required to address clinical needs across disease care plans tailored to different geographies and ethnicities. Innovation Think Tank (ITT) teams across multiple locations formed focus groups to elaborate the pathways of 22 global diseases, selected based on ranking of associated economic burden and threat to life. Ideation sessions were held to identify pain points and find innovative solutions. Additionally, inputs were taken from co-creation sessions at universities worldwide. The optimization and design of infographics and care plan was done based on the key information gathered-facts and figures, stakeholders, pain points and solutions. Finally, validation was obtained from clinical and technology experts globally. A disease pathway framework was created to develop pathways for 22 global diseases. Over 1,500 pain points were collected and about 1,900 ideas were proposed. The approach was applied to optimize its application to 30 product and portfolio definition projects over 2 years at Siemens Healthineers, as well as co-creation programs with universities and hospitals. The disease pathway framework provides a unique foundation for extensive collaboration among multiple stakeholders, through information sharing and delivering high-quality solutions based on the identified problems and customer needs.

Flexible Electronics and Devices as Human-Machine Interfaces for Medical Robotics

Medical robots are invaluable players in non-pharmaceutical treatment of disabilities. Particularly, using prosthetic and rehabilitation devices with human-machine interfaces can greatly improve the quality of life for impaired patients. In recent years, flexible electronic interfaces and soft robotics have attracted tremendous attention in this field due to their high biocompatibility, functionality, conformability, and low-cost. Flexible human-machine interfaces on soft robotics will make a promising alternative to conventional rigid devices, which can potentially revolutionize the paradigm and future direction of medical robotics in terms of rehabilitation feedback and user experience. In this review, the fundamental components of the materials, structures, and mechanisms in flexible human-machine interfaces are summarized by recent and renowned applications in five primary areas: physical and chemical sensing, physiological recording, information processing and communication, soft robotic actuation, and feedback stimulation. This review further concludes by discussing the outlook and current challenges of these technologies as a human-machine interface in medical robotics.

Conception and realization of an IoT-enabled deep CNN decision support system for automated arrhythmia classification

Arrhythmias are irregular heartbeats that may be life-threatening. Proper monitoring and the right care at the right time are necessary to keep the heart healthy. Monitoring electrocardiogram (ECG) patterns on continuous monitoring devices is time-consuming. An intense manual inspection by caregivers is not an option. In addition, such an inspection could result in errors and inter-variability. This article proposes an automated ECG beat classification method based on deep neural networks (DNN) to aid in the detection of cardiac arrhythmias. The data collected by an Internet of Things enabled ECG monitoring device are transferred to a server. They are analysed by a deep learning model, and the results are shared with the primary caregiver. The proposed model is trained using the MIT-BIH ECG arrhythmia database to classify into four classes: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), and premature ventricular contraction (V). The received data are sampled with an overlapping sliding window and divided into an 80:20 ratio for training and testing, with tenfold cross-validation. The proposed method achieves higher accuracy with a simple model without any preprocessing when compared with previous works. For the train and test sets, we achieved accuracy rates of 99.09 and 99.03%, respectively. A precision, recall, and F1 scores of 0.99 is obtained. The proposed model achieves its goal of developing a simple and accurate ECG monitoring system with improved performance. This simple and efficient deep learning approach for heartbeat classification could be applied in real-time telehealth monitoring systems.

Long-Term Polygraphic Monitoring through MEMS and Charge Transfer for Low-Power Wearable Applications

In this work, we propose a wireless wearable system for the acquisition of multiple biopotentials through charge transfer electrostatic sensors realized in MEMS technology. The system is designed for low power consumption and low invasiveness, and thus candidates for long-time monitoring in free-living conditions, with data recording on an SD or wireless transmission to an external elaborator. Thanks to the wide horizon of applications, research is very active in this field, and in the last few years, some devices have been introduced on the market. The main problem with those devices is that their operation is time-limited, so they do not match the growing demand for long monitoring, which is a must-have feature in diagnosing specific diseases. Furthermore, their versatility is hampered by the fact that they have been designed to record just one type of signal. Using ST-Qvar sensors, we acquired an electrocardiogram trace and single-channel scalp electroencephalogram from the frontal lobes, together with an electrooculogram. Excellent results from all three types of acquisition tests were obtained. The power consumption is very low, demonstrating that, thanks to the MEMS technology, a continuous acquisition is feasible for several days.

Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review

Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.

Remote Cardiac Rhythm Monitoring in the Era of Smart Wearables: Present Assets and Future Perspectives

Remote monitoring and control of heart function are of primary importance for patient evaluation and management, especially in the modern era of precision medicine and personalized approach. Breaking technological developments have brought to the frontline a variety of smart wearable devices, such as smartwatches, chest patches/straps, or sensors integrated into clothing and footwear, which allow continuous and real-time recording of heart rate, facilitating the detection of cardiac arrhythmias. However, there is great diversity and significant differences in the type and quality of the information they provide, thus impairing their integration into daily clinical practice and the relevant familiarization of practicing physicians. This review will summarize the different types and dominant functions of cardiac smart wearables available in the market. Furthermore, we report the devices certified by official American and/or European authorities and the respective sources of evidence. Finally, we comment pertinent limitations and caveats as well as the potential answers that flow from the latest technological achievements and future perspectives.