Wearable Technologies and AI at the Far Edge for Chronic Heart Failure Prevention and Management: A Systematic Review and Prospects

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors

The role of electrocardiography in occupational medicine, from einthoven’s invention to the digital era of wearable devices

Clinical-instrumental investigations, such as electrocardiography (ECG), represent a corollary of a procedures that, nowadays, is called upon as part of the principles of precision medicine. However when carrying out the professional routine examinations, most tend to ignore how a “simple” instrument can offer indispensable support in clinical practice, even in occupational medicine. The advent of the digital age, made of silicon and printed circuit boards, has allowed the miniaturization of the electronic components of these electro-medical devices. Finally, the adoption of patient wearables in medicine has been rapidly expanding worldwide for a number of years. This has been driven mainly by consumers’ demand to monitor their own health. With the ongoing research and development of new features capable of assessing and transmitting real-time biometric data, the impact of wearables on cardiovascular management has become inevitable. Despite the potential offered by this technology, as evident from the scientific literature, the application of these devices in the field of health and safety in the workplace is still limited. This may also be due to the lack of targeted scientific research. While offering great potential, it is very important to consider and evaluate ethical aspects related to the use of these smart devices, such as the management of the collected data relating to the physiological parameters and the location of the worker. This technology is to be considered as being aimed at monitoring the subject’s physiological parameters, and not at the diagnosis of any pathological condition, which should always be on charge of the medical specialist We conducted a review of the evolution of the role that electrophysiology plays as part of occupational health and safety management and on its possible future use, thanks to ongoing technological innovation

The utility of handheld and wearable devices in the diagnosis of cardiac arrhythmias

The aim of this thesis is to highlight the existing body of literature on the utility of wearable and handheld devices in the diagnosis and management of cardiac arrhythmias. Furthermore, the thesis investigates the accuracy and utility of the AliveCor Kardia for the detection of cardiac arrhythmias in a systematic fashion

The need to promote sleep health in public health agendas across the globe.

Healthy sleep is essential for physical and mental health, and social wellbeing; however, across the globe, and particularly in developing countries, national public health agendas rarely consider sleep health. Sleep should be promoted as an essential pillar of health, equivalent to nutrition and physical activity. To improve sleep health across the globe, a focus on education and awareness, research, and targeted public health policies are needed. We recommend developing sleep health educational programmes and awareness campaigns; increasing, standardising, and centralising data on sleep quantity and quality in every country across the globe; and developing and implementing sleep health policies across sectors of society. Efforts are needed to ensure equity and inclusivity for all people, particularly those who are most socially and economically vulnerable, and historically excluded

Risks and risk monitoring in sotalol therapy for atrial fibrillation

Background: Atrial fibrillation (AF) stands as the most prevalent arrhythmia, significantly impacting both the prognosis and symptomatology of affected individuals. Healthcare expenditures associated with AF treatment and its related complications, in terms of morbidity and mortality, are substantial. In symptomatic AF, or in AF-induced left ventricular dysfunction, the primary treatment objective is to restore sinus rhythm. Earlier studies suggested equivalence between rate and rhythm control, but contemporary research points toward rhythm control being associated with lower rates of cardiovascular hospitalization and events. Cardioversion (CV) is often necessitated and is most effective when combined with an antiarrhythmic drug. Sotalol, a potent Ikr blocker, is one of the recommended drugs to prevent AF relapse. However, sotalol carries an inherent risk of proarrhythmias and sudden death. The proarrhythmic risk associated with sotalol in guideline-selected patients undergoing contemporary management remains unknown. Prolongation of the QT interval, measured on ECG, is considered the most reliable risk marker for ventricular arrhythmias in sotalol treatment. The dynamicity of the QT interval in patients receiving Ikr blocking drugs is poorly studied. The thesis aimed to 1) evaluate the QT interval in patients after CV of AF with sotalol treatment and 2) compare mortality and the incidence of ventricular arrhythmias in patients following CV of AF who receive sotalol or beta-blockers. Methods and results: Study I. Triplicate ECGs were recorded one hour after CV and one week later in 208 patients receiving a steady dose of sotalol or beta-blocker (metoprolol) treatment. In sotalol-treated patients, the mean QTc interval (QT corrected for heart rate) decreased during the week after CV (-20.3 ±24 ms), whereas no significant change was observed in metoprolol-treated patients (-2.5 ±18 ms). Longer QTc interval after CV and better renal function were associated with the reduction in QTc. Study II. Twenty-four hour, 12-lead Holter recordings were conducted after CV in 50 patients treated with sotalol or metoprolol. Diurnal analysis of QTc revealed that 22% of sotalol-treated patients had >20% of all heartbeats with prolonged QTc >500 ms, primarily occurring during nighttime, compared to no patients treated with metoprolol. Diurnal variations were observed in both HR and QTc. Study III. A nationwide register-based cohort study involving all Swedish AF patients included after their second CV from 2006 to 2017. Patients receiving sotalol (n=4,987) and cardioselective beta-blocker-treated patients (n=27,078), were followed for an average of 458 days. A diagnosis of heart failure was found in 14% of patients. All-cause mortality was lower in sotalol-treated patients, a difference that persisted in the propensity-matched comparison (n=4,953 in each group) with an incidence rate (IR) of 1.19 (0.93-1.49) vs. 2.01 (1.67-2.39) deaths per 100 patient years, and IRR of 0.59 (0.44-0.79). No differences were observed in ventricular arrhythmias with an IR of 1.38 (1.10-1.71) vs. 1.26 (1.00-1.57) events per 100 patient years, and an IRR of 1.59 (0.85-2.99). Conclusions and summary: In AF patients after CV, selected for sotalol treatment after 2006, mortality or ventricular arrhythmias were not increased compared to patients treated with a cardioselective beta-blocker (Study III). The QTc interval significantly decreased during the week following CV to sinus rhythm in sotalol-treated patients (Study 1). Patients on sotalol exhibited a substantial number of heartbeats with prolonged QTc over 24 hours, particularly at night. QT dynamicity over 24 hours was evident in sotalol-treated patients, although the impact of the HR correction formula remains unclear (Study II). These findings could provide insight into the increased risk of proarrhythmias immediately after CV and indicate that the QT interval is a dynamic measure. Careful patient selection and the avoidance of congestive heart failure likely minimize the risks associated with sotalol treatment

CIRCADIAN COMPUTING: SENSING AND STABILIZING BIOLOGICAL RHYTHMS

This dissertation lays the groundwork for Circadian Computing with a novel and broad vision of technologies that support and adapt to our innate biological rhythms. Similar to most terrestrial organisms, human physiology and behavior are shaped by a 24-hour periodicity known as circadian rhythm. Indeed, almost every neurobehavioral process including our sleep, metabolism, cognitive performance, and mood reflects circadian rhythms. These rhythms ensure synchronization across different processes and as such, are crucial for our health and well-being. Persistent circadian disruption increases risk for cancer, obesity, and cardiovascular diseases. It has been associated with occupational accidents and serious loss of productivity in the workplace as well. Recent findings have also started identifying links between circadian disruption and mental illnesses including bipolar disorder and schizophrenia. However, in our modern world, circadian disruption is becoming increasingly widespread. The invention of artificial light fundamentally changed our ancestral sleep and wakeup patterns. Since then, we have gradually moved towards a 24-hour society. The recent development in entertainment and communication technologies has also resulted in an “always-on” ethos. The resulting trend is worrisome. Sleep pathologies are reaching an epidemic level with 70\% of the population suffering from significant circadian disruptions. As a result, recently there has been an increased focus on monitoring and identifying disruptions in circadian rhythms. However, these methods and findings are often limited to controlled lab environments. As a result, they are not adequate for granular monitoring of circadian disruptions in the wild over a longitudinal period of time. As such, there is a need for novel pervasive technologies for tracking, monitoring, and modeling circadian disruptions and its impact in the real world. There is also an opportunity for developing intervention tools for maintaining circadian stability. This dissertation is a leading step towards the broad vision of circadian-aware technologies for sensing, adapting to, and stabilizing our innate biological rhythms. In my PhD work, I have shown the feasibility of bringing a circadian-aware perspective across different application domains. Specifically, I have developed and evaluated methods for unobtrusively assessing circadian disruptions. I have also showed that behavioral and contextual data can be used for modeling and predicting alertness — a circadian process integral to our cognitive performance. I have also developed, deployed, and evaluated a data driven tool focusing on identifying circadian anomalies in patients with bipolar disorder. With this groundwork in place, I believe that there is an exciting opportunity lying ahead for Circadian Computing. In particular, circadian-aware technologies can potentially reshape a number of application domains including education and learning, optimized scheduling, mental health care, and chronotherapy. I hope this dissertation motivates a circadian perspective in future technology development and contributes to the shared effort of improving our productivity, health, and well-being

INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain