Antepartum Fetal Monitoring through a Wearable System and a Mobile Application

Prenatal monitoring of Fetal Heart Rate (FHR) is crucial for the prevention of fetal pathologies and unfavorable deliveries. However, the most commonly used Cardiotocographic exam can be performed only in hospital-like structures and requires the supervision of expert personnel. For this reason, a wearable system able to continuously monitor FHR would be a noticeable step towards a personalized and remote pregnancy care. Thanks to textile electrodes, miniaturized electronics, and smart devices like smartphones and tablets, we developed a wearable integrated system for everyday fetal monitoring during the last weeks of pregnancy. Pregnant women at home can use it without the need for any external support by clinicians. The transmission of FHR to a specialized medical center allows its remote analysis, exploiting advanced algorithms running on high-performance hardware able to obtain the best classification of the fetal condition. The system has been tested on a limited set of pregnant women whose fetal electrocardiogram recordings were acquired and classified, yielding an overall score for both accuracy and sensitivity over 90%. This novel approach can open a new perspective on the continuous monitoring of fetus development by enhancing the performance of regular examinations, making treatments really personalized, and reducing hospitalization or ambulatory visits. Keywords: tele-monitoring; wearable devices; fetal heart rate; telemedicin

On the Design and Evaluation of an eHealth System for Management of Patients in Out-of-Hospital Care

This thesis covers the design and evaluation of a generic web-based eHealth system into real clinical settings. Moreover, the use of an off-the-shelf video communication system for out-of-hospital care is tested and evaluated. A large part of the thesis will cover the design process of the web-based system (WBS) for out-of- hospital long term disease management. The systems were implemented in two very different settings: heart failure care and neonatal home healthcare. The methods of evaluation are questionnaires, including both patients and nurses. Also, data extracted from a blood pressure monitor, and data extracted from the prototype system database are used in the evaluation. The first study, performed in 2008, indicated that the prototype developed was applicable in the patient group (heart failure). However, several issues concerning the system were found, resulting in the development of a new prototype system. The two subsequent studies, heart failure care and neonatal home healthcare, were performed using the new system. Results from these two studies indicate that the WBS is usable for two very different applications. In heart failure care compliance with the system is very good, however in neonatal care the results are ambiguous. The neonatal evaluations show that even though the patients may be positive towards eHealth systems, the necessity of care personnel participation is vital. If there is no feedback, the patients lose interest and find the system useless. A questionnaire survey studying attitudes towards information and communication technology (ICT) as a tool in health care, and also studying the attitudes towards home follow up, was also performed. The target group was healthcare personnel in heart care, and the questionnaire was sent out to 84 cardiology and medicine clinics in Sweden. All 21 counties and regions in Sweden were included in the dispatch, and of these responses were collected from 17. The results indicate a large interest and confidence in healthcare ICT, and well as in home follow-up and monitoring of patients. A comparison between nurses and physicians indicate a slight difference where nurses in general are more positive than physicians

Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months

Digital solutions for self-monitoring physical health and wellbeing during pregnancy

Perinatal disorders were among the top ten causes of global burden of disease in 2019. Better access to perinatal healthcare would help to reduce preventable morbidity. The increase in access to and use of smartphones presents a unique opportunity to transform and improve how women monitor their own health during pregnancy. This thesis aims to investigate the quality and usage of currently available pregnancy digital health tools for self-monitoring and to validate a newly developed, custom-built pregnancy self-monitoring tool. In Chapter 2, the most popular, commercially available pregnancy apps and their monitoring tools were evaluated for their quality by conducting a pregnancy app scoping review. In Chapters 3 and 4, pregnant women and healthcare professionals were surveyed and interviewed to better understand their usage of and attitudes towards digital health, as well as their thoughts about two hypothetical app features (a direct patient-to-healthcare professional communication tool and a novel body measurement tool). In Chapter 5, we test the performance of a first generation, custom-built body measurement tool (which we called BMT-1) by comparing the digital measurements extracted from photos taken on smartphones to physical measurements taken with measuring tape. The performance of BMT-1 was also assessed on a longitudinal set of digitally constructed pregnancy models. Collectively, the findings from Chapters 2, 3 and 4 provide evidence that there is both opportunity and scope for the development of new digital health tools to support and enhance the quality of care during pregnancy. The results from Chapter 5 indicate that BMT-1 successfully extracted body measurements from both photos and digitally constructed pregnancy models, though would require refinement before it could be launched. To finalise, in Chapter 6, I outline how these findings could help to guide the design, development and implementation of new pregnancy digital health tools