Ebook: pHealth 2016
Smart mobile systems, eHealth and telemedicine, as well as social media and gamification, have all become important enablers for the provision of the next generation of health services.
This book presents the proceedings of the 13th International Conference on Wearable, Micro and Nano Technologies for Personalised Health (pHealth 2016), held in Heraklion, Crete, in May 2016. pHealth 2016 brings together experts from medical, technological, political, administrative, legal and social domains with the aim of further emphasizing the integration of biology and medical data, systems and information using mobile technologies. The book includes two keynotes and two specially invited talks as well as 21 oral and 10 poster presentations selected by a rigorous review process (with a rejection rate of more than 30%) from the more than 45 submissions to the conference. The book is divided into two sections. The first covers mHealth, devices, applications and biosensors and the second deals with smart personal health systems, deep learning, interoperability and precision medicine. Subjects covered include the development of micro-, nano-, bio- and smart-systems with an emphasis on personalized health, virtual care, precision medicine, big bio data management and analytics, as well as security, privacy and safety issues.
This book will be of interest to all those whose work involves the provision of healthcare, both today and into the future.
The pHealth 2016 Conference is the 13th in a series of scientific events bringing together expertise from medical, technological, political, administrative, legal and social domains. pHealth 2016 opens a new chapter in the success story of the series of international conferences on wearable or implantable micro, nano and biotechnologies for personalized health. Starting in 2003 with personal health management systems, pHealth has continuously extended its scope evolving to a truly interdisciplinary event by covering technological and biomedical facilities, legal, ethical, social, and organizational requirements and impacts as well as necessary basic research for enabling the future proof care paradigms. Thereby, pHealth increasingly combines medical devices and eHealth based services with public health, prevention, social and elderly care, wellness and personal fitness to establish participatory, predictive, personalized, preventive, and effective care settings. Smart mobile systems, eHealth and telemedicine have become important enablers for ubiquitous pervasive health as the next generation health services. Social media and gamification has added even further knowledge to pHealth both as a business domain and as a community safety-net.
The pHealth2016 conference aims to further emphasise on the integration of biology and medical data, systems and information using mobile technologies through the development of micro-nano-bio smart systems for pHealth emphasizing on personalized health, virtual care, precision medicine, big bio-data management and analytics as well as security, privacy and safety issues.
We welcome you to the city of Heraklion on the beautiful island of Crete for the pHealth 2016 conference to share experiences and results, and to open up for the future!
Following a long-term tradition, pHealth 2016 is supported by the European Federation for Medical Informatics (EFMI)but also the HL7 International. The pHealth 2016 presentations are complemented by demonstrations of practical artifacts and solutions as well as by a students' poster competition. This proceedings volume covers 2 keynotes and 2 specially invited talks, but also 21 oral presentations selected from more than 45 submissions to the pHealth 2015 conference, and 10 poster presentations. All submissions have been carefully and critically reviewed by at least two independent experts from other than the authors' home countries, and additionally by at least one member of the Scientific Program Committee. The performed highly selective review process resulted in a full papers rejection rate of more than 30%, thereby guaranteeing a high scientific level of the pHealth 2016 published papers. The editors are indebted to the acknowledged and highly experienced reviewers for having essentially contributed to the quality of the conference and the book at hand. Both the pHealth 2016 Conference and the publication of the pHealth 2016 proceedings at IOS Press would not have been possible without the supporters and sponsors Health Level 7 International (HL7 International), and European Federation for Medical Informatics (EFMI). The editors are also grateful to the dedicated efforts of the Local Organizing Committee members and their supporters for carefully and smoothly preparing and operating the conference. They especially thank all team members from IMBB-FORTH and CERTH/INAB-AUTH Lab of Computing & Medical Informatics, for their dedication to the event.
Nicos Maglaveras, Electra Gizeli (Editors)
The European Patient Summary (PS) guideline specifies a minimal dataset of essential and important information for unplanned or emergency care initially defined in the epSOS project with aim to improve patients' safety and quality of Care. The eHealth Network of European Union (EU) Member State (MS) representatives established under Article 14 of the EU directive 2011/24 on patient rights to cross-border healthcare adopted PS guideline in November 2013 and since then the guideline has been part of MS strategic eHealth implementation plans, standardization efforts, and concrete regional, national, European and international projects. This paper reviews implementation efforts for the implementation of an operational patient summary service in Greece drawing on challenges and lessons learned for sustainable standards-based large scale eHealth deployment in Europe and abroad, as well as the reuse of best practices from international standards and integration profiles.
Although Europe ‘produces’ excellent science, it has not been equally successful in translating scientific results into commercially successful companies in spite of European and national efforts invested in supporting the translation process. The Idea-to-Market process is highly complex due to the large number of actors and stakeholders. ITECH was launched to propose recommendations which would accelerate the Idea-to-Market process of health technologies leading to improvements in the competitiveness of the European health technology industry in the global markets. The project went through the following steps: defining the Idea-to-Market process model; collection and analysis of funding opportunities; identification of 12 gaps and barriers in the Idea-to-Market process; a detailed analysis of these supported by interviews; a prioritization process to select the most important issues; construction of roadmaps for the prioritized issues; and finally generating recommendations and associated action plans. Seven issues were classified as in need of actions. Three of these are part of the ongoing Medical Device Directive Reform (MDR), namely health technology assessment, post-market surveillance and regulatory process, and therefore not within the scope of ITECH. Recommendations were made for eHealth taxonomy; Education and training; Clinical trials and Adoption space and Human Factors Engineering (HFE).
The employment of personal health systems (pHealth) is a valuable concept in the management of chronic diseases, particularly in the context of cardiovascular diseases. By means of a continuous monitoring of the patient it is possible to seamless access multiple sources of data, including physiological signals, providing professionals with a global and reliable view of the patient's status. In practice, it is possible the prompt diagnosis of events, the early prediction of critical events and the implementation of personalized therapies. Furthermore, the information collected during long periods creates new opportunities in the diagnosis of a disease, in its evolution, and in the prediction of possible complications.
The focus of this work is the research and implementation of multi-parametric algorithms for data analysis in pHealth context, including data mining techniques as well as physiological signal modelling and processing. In particular, fusion strategies for cardiovascular status evaluation (namely cardiovascular risk assessment and cardiac function estimation) and multi-parametric prediction algorithms for the early detection of cardiovascular events (such as hypertension, syncope and heart failure decompensation) will be addressed.
Heart failure (HF) is commonly a chronic condition associated with frequent hospital admissions. Early knowledge about a possible deterioration of this condition would enable early treatment for the prevention of adverse events and related hospital admissions. In this paper we present a computational method for predictive information extraction from daily physiological signals, which can be obtained by a telemonitoring system with wearable sensors. It is based on wavelet analysis of temporal signal patterns. Experiments with data from patients enrolled in a telemonitoring protocol show that the proposed method is capable of predicting HF hospitalization events one day before they happen, even in the case of low compliance to the protocol. These results indicate a promising perspective towards a monitoring system that would provide improved life quality for HF patients.
The aim of this pilot study was to investigate the possibility to find a correlation between the output from a portable pedobarography system and the walking intensity expressed as walking speed. The system uses shoe insoles with force sensing resistors and wireless transmission of the data via Bluetooth. The force-time integral, at the toe-off phase of the step, for the force sensors in the forward part of the right foot was used to measure impulse data for 10 subjects performing walks in three different walking speeds. This data was then corrected by multiplication with the step frequency. This pilot study indicates that the portable pedobarography system output shows a linear relationship with the walking intensity expressed as walking speed on an individual level.
One of the most common knee joint disorders is known as osteoarthritis which results from the progressive degeneration of cartilage and subchondral bone over time, affecting essentially elderly adults. Current evaluation techniques are either complex, expensive, invasive or simply fails into detection of small and progressive changes that occur within the knee. Vibroarthrography appeared as a new solution where the mechanical vibratory signals arising from the knee are recorded recurring only to an accelerometer and posteriorly analyzed enabling the differentiation between a healthy and an arthritic joint. In this study, a vibration-based classification system was created using a dataset with 92 healthy and 120 arthritic segments of knee joint signals collected from 19 healthy and 20 arthritic volunteers, evaluated with k-nearest neighbors and support vector machine classifiers. The best classification was obtained using the k-nearest neighbors classifier with only 6 time-frequency features with an overall accuracy of 89.8% and with a precision, recall and f-measure of 88.3%, 92.4% and 90.1%, respectively. Preliminary results showed that vibroarthrography can be a promising, non-invasive and low cost tool that could be used for screening purposes. Despite this encouraging results, several upgrades in the data collection process and analysis can be further implemented.
Rehabilitation is important for patients with cardiovascular diseases (CVD) to improve health outcomes and quality of life. However, adherence to current exercise programmes in cardiac rehabilitation is limited. We present the design and development of a Decision Support System (DSS) for telerehabilitation, aiming to enhance exercise programmes for CVD patients through ensuring their safety, personalising the programme according to their needs and performance, and motivating them toward meeting their physical activity goals. The DSS processes data originated from a Microsoft Kinect camera, a blood pressure monitor, a heart rate sensor and questionnaires, in order to generate a highly individualised exercise programme and improve patient adherence. Initial results within the EU-funded PATHway project show the potential of our approach.
Research progressing during the last decade focuses more on non-contact based systems to monitor Heart Rate (HR) which are simple, low-cost and comfortable to use. Most of the non-contact based systems are using RGB videos which is suitable for lab environment. However, it needs to progress considerably before they can be applied in real life applications. As luminance (light) has significance contribution on RGB videos HR monitoring using RGB videos are not efficient enough in real life applications in outdoor environment. This paper presents a HR monitoring method using Lab color facial video captured by a webcam of a laptop computer. Lab color space is device independent and HR can be extracted through facial skin color variation caused by blood circulation considering variable environmental light. Here, three different signal processing methods i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA) have been applied on the color channels in video recordings and blood volume pulse (BVP) has been extracted from the facial regions. In this study, HR is subsequently quantified and compare with a reference measurement. The result shows that high degrees of accuracy have been achieved compared to the reference measurements. Thus, this technology has significant potential for advancing personal health care, telemedicine and many real life applications such as driver monitoring.
Malaria is a life-threatening disease that caused more than 400,000 deaths in sub-Saharan Africa in 2015. Mass prevention of the disease is best achieved by vector control which heavily relies on the use of insecticides. Monitoring mosquito vector populations is an integral component of control programs and a prerequisite for effective interventions. Several individual methods are used for this task; however, there are obstacles to their uptake, as well as challenges in organizing, interpreting and communicating vector population data. The Horizon 2020 project “DMC-MALVEC” consortium will develop a fully integrated and automated multiplex vector-diagnostic platform (LabDisk) for characterizing mosquito populations in terms of species composition, Plasmodium infections and biochemical insecticide resistance markers. The LabDisk will be interfaced with a Disease Data Management System (DDMS), a custom made data management software which will collate and manage data from routine entomological monitoring activities providing information in a timely fashion based on user needs and in a standardized way. The ResistanceSim, a serious game, a modern ICT platform that uses interactive ways of communicating guidelines and exemplifying good practices of optimal use of interventions in the health sector will also be a key element. The use of the tool will teach operational end users the value of quality data (relevant, timely and accurate) to make informed decisions. The integrated system (LabDisk, DDMS & ResistanceSim) will be evaluated in four malaria endemic countries, representative of the vector control challenges in sub-Saharan Africa, (Cameroon, Ivory Coast, Ethiopia and Zambia), highly representative of malaria settings with different levels of endemicity and vector control challenges, to support informed decision-making in vector control and disease management.
Global healthcare systems are struggling with the enormous burden associated with infectious diseases, as well as the incessant rise of antimicrobial resistance. In order to adequately address these issues, there is an urgent need for rapid and accurate infectious disease diagnostics. The H2020 project DIAGORAS aims at diagnosing oral and respiratory tract infections using a fully integrated, automated and user-friendly platform for physicians' offices, schools, elderly care units, community settings, etc. Oral diseases (periodontitis, dental caries) will be detected via multiplexed, quantitative analysis of salivary markers (bacterial DNA and host response proteins) for early prevention and personalised monitoring. Respiratory Tract Infections will be diagnosed by means of DNA/RNA differentiation so as to identify their bacterial or viral nature. Together with antibiotic resistance screening on the same platform, a more efficient treatment management is expected at the point-of-care. At the heart of DIAGORAS lies a centrifugal microfluidic platform (LabDisk and associated processing device) integrating all components and assays for a fully automated analysis. The project involves an interface with a clinical algorithm for the comprehensive presentation of results to end-users, thereby increasing the platform's clinical utility. DIAGORAS' performance will be validated at clinical settings and compared with gold standards.
The development of integrated, fast and affordable platforms for pathogen detection is an emerging area where a multidisciplinary approach is necessary for designing microsystems employing miniaturized devices; these new technologies promise a significant advancement of the current state of analytical testing leading to improved healthcare. In this work, the development of a lab-on-chip microsystem platform for the genetic analysis of Salmonella in milk samples is presented. The heart of the platform is an acoustic detection biochip, integrated with a microfluidic module. This detection platform is combined with a micro-processor, which, alongside with magnetic beads technology and a DNA micro-amplification module, are responsible for performing sample pre-treatment, bacteria lysis, nucleic acid purification and amplification. Automated, multiscale manipulation of fluids in complex microchannel networks is combined with novel sensing principles developed by some of the partners. This system is expected to have a significant impact in food-pathogen detection by providing for the first time an integrated detection test for Salmonella screening in a very short time. Finally, thanks to the low cost and compact technologies involved, the proposed set-up is expected to provide a competitive analytical platform for direct application in field settings.
The decision-making is a key event in the clinical practice. The program products with clinical decision support models in electronic data-base as well as with fixed decision moments of the real clinical practice and treatment results are very actual instruments for improving phthisiological practice and may be useful in the severe cases caused by the resistant strains of Mycobacterium tuberculosis. The methodology for gathering and structuring of useful information (critical clinical signals for decisions) is described. Additional coding of clinical diagnosis characteristics was implemented for numeric reflection of the personal situations. The created methodology for systematization and coding Clinical Events allowed to improve the clinical decision models for better clinical results.
A key clinical challenge is to determine the desired ‘dry weight’ of a patient in order to terminate the dialysis procedure at the optimal moment and thus avoid the effects of over- and under-hydration. It has been found that the effects of haemodialysis on patients can be conveniently monitored using whole-body bioimpedance measurements. The identified need of assessing the hydrational status of patients undergoing haemodialysis at home gave rise to the present Dialydom (DIALYse à DOMicile) project. The aim of the project is to develop a convenient miniaturised impedance monitoring device for localised measurements (on the calf) in order to estimate an impedimetric hydrational index of the home-based patient, and to transmit this and other parameters to a remote clinical site. Many challenges must be overcome to develop a robust and valid home-based device. Some of these are presented in the paper.
The increasing availability of low cost and easy to use personalized medical monitoring devices has opened the door for new and innovative methods of health monitoring to emerge. Cuff-less and continuous methods of measuring blood pressure are particularly attractive as blood pressure is one of the most important measurements of long term cardiovascular health. Current methods of noninvasive blood pressure measurement are based on inflation and deflation of a cuff with some effects on arteries where blood pressure is being measured. This inflation can also cause patient discomfort and alter the measurement results. In this work, a mobile application was developed to collate the PhotoPlethysmoGramm (PPG) waveform provided by a pulse oximeter and the electrocardiogram (ECG) for calculating the pulse transit time. This information is then indirectly related to the user's systolic blood pressure. The developed application successfully connects to the PPG and ECG monitoring devices using Bluetooth wireless connection and stores the data onto an online server. The pulse transit time is estimated in real time and the user's systolic blood pressure can be estimated after the system has been calibrated. The synchronization between the two devices was found to pose a challenge to this method of continuous blood pressure monitoring. However, the implemented continuous blood pressure monitoring system effectively serves as a proof of concept. This combined with the massive benefits that an accurate and robust continuous blood pressure monitoring system would provide indicates that it is certainly worthwhile to further develop this system.
In this article novel approaches for the improvement of the recorded signal coupled with the feasibility of multiple analyte detection, irrespective of the biosensor platform are being presented. The techniques that have been developed address commonly encountered issues that have traditionally hindered the commercialization of biosensors, such as cost, reproducibility and sensitivity and most importantly multianalyte detection. The fluorescence-based detection of copper is being described as an example of the use of Laser Induced Forward Transfer technique (LIFT) for the immobilization of biomolecules with high spatial resolution, in addition to a technique that involves the displacement of a short complementary strand to the immobilized probe molecule for the quantification of analyte binding and the enhancement of the recorded signal.
Perceiving and identifying emotions on facial expressions is one of the basic abilities that compose emotional intelligence, and is crucial for normal social functions. It is well documented that facial expression conveys information about felt emotion, and that expressive behavior can activate or regulate the emotion required by a given situation. Instruments measuring emotion perception based on facial expression have been found in literature either as stand-alone scales or as part of other tests. The proposed tool expands existing instruments to combine online availability while affording assessment of emotion recognition on a continuum of intensity. It was founded on Ekman's Facial Action Units, with two Virtual Characters (male and female) portraying five basic emotions Anger, Disgust, Fear, Joy, Sadness, plus Neutral expression. The user can navigate on the custom-made pentagon and choose the emotion and intensity level (1–5) through a single click. The preliminary evaluation of the tool on thirty normal subjects provided threshold data that can later be used as benchmarks to assess emotion perception sensitivity in psychiatric disorders such as depression and schizophrenia characterized by emotional dysfunction.
ITEA2 project CareWare approach efficiently perform wearable sensor data processing and data fusion in order to visualize the holistic view of user's health and training status personalized, intuitively and trustworthy way and give feedback for user about individualized intensity and time of exercising control.
A fall is a multifactorial phenomenon which cause an increase in both mortality and injury rates. The cause of a fall is mostly related to loss in reflexes especially in older ages. A number of large prospective studies shows that elderly patients have significant fractures and injuries even sometimes in some cases a fall can be concluded with deaths. However, in case of fall, if the situation is noticed and aided quickly, the life quality can be increased significantly in older people. With implementation of preventive strategies or premonitory devices, this devastating problem can be solved.
The IOT project is a prototype with two versions which are needled and attached versions and accomplishes basic functions such as information about falls and send it through the internet. By this way, the falls are transmitted to concerned people or patient's relatives with position information.
The development of platforms that are able to continuously monitor and handle epileptic seizures in a non invasive manner is of great importance as they would improve the quality of life of drug resistant epileptic patients. In this work, a device and a computational platform is presented for acquiring low noise electroencephalographic signals, for the detection/prediction of epileptic seizures and the storage of ictal activity in an electronic personal health record. In order to develop this platform, a systematic clinical protocol was established including a number of drug resistant children from the University Hospital of Heraklion. Dry electrodes with innovative micro-spike design were proposed in order to increase the signal to noise ratio of the recorded EEG signals. A wearable low cost platform and its corresponding wireless communication protocol was developed focus on minimizing the interference with the patient's body. A computational subsystem with advanced algorithms provides detection/anticipation of upcoming seizure activity and aims to protect the patient from an accident due to a seizure or to improve his/her social life. Finally, the seizure activity information is stored in an electronic health record for further clinical evaluation.
Integrated care and connected health are two fast evolving concepts that have the potential to leverage personalised health. From the one side, the restructuring of care models and implementation of new systems and integrated care programs providing coaching and advanced intervention possibilities, enable medical decision support and personalized healthcare services. From the other side, the connected health ecosystem builds the means to follow and support citizens via personal health systems in their everyday activities and, thus, give rise to an unprecedented wealth of data. These approaches are leading to the deluge of complex data, as well as in new types of interactions with and among users of the healthcare ecosystem. The main challenges refer to the data layer, the information layer, and the output of information processing and analytics. In all the above mentioned layers, the primary concern is the quality both in data and information, thus, increasing the need for filtering mechanisms. Especially in the data layer, the big biodata management and analytics ecosystem is evolving, telemonitoring is a step forward for data quality leverage, with numerous challenges still left to address, partly due to the large number of micro-nano sensors and technologies available today, as well as the heterogeneity in the users' background and data sources. This leads to new R&D pathways as it concerns biomedical information processing and management, as well as to the design of new intelligent decision support systems (DSS) and interventions for patients. In this paper, we illustrate these issues through exemplar research targeting chronic patients, illustrating the current status and trends in PHS within the integrated care and connected care world.
Information in the healthcare domain and in particular personal health record information is heterogeneous by nature. Clinical, lifestyle, environmental data and personal preferences are stored and managed within such platforms. As a result, significant information from such diverse data is difficult to be delivered, especially to non-IT users like patients, physicians or managers. Another issue related to the management and analysis is the volume, which increases more and more making the need for efficient data visualization and analysis methods mandatory. The objective of this work is to present the architectural design for seamless integration and intelligent analysis of distributed and heterogeneous clinical information in the PHR context, as a result of a requirements elicitation process in iManageCancer project. This systemic approach aims to assist health-care professionals to orient themselves in the disperse information space and enhance their decision-making capabilities, to encourage patients to have an active role by managing their health information and interacting with health-care professionals.
Personalized medicine should target not only the genetic and clinical aspects of the individual patients but also the different cognitive, psychological, family and social factors involved in various clinical choices. To this direction, in this paper, we present instruments to assess the psycho-emotional status of cancer patients and to evaluate the resilience in their family constructing in such a way an augmented patient profile. Using this profile, 1) information provision can be tailored according to patients characteristics; 2) areas of functioning can be monitored both by the patient and by the clinicians, providing suggestions and alerts; 3) personalized decision aids can be develop to increase patient's participation in the consultation process with their physicians and improve their satisfaction and involvement in the decision-making process. Our preliminary evaluation shows promising results and the potential benefits of the tools.