mHealth Engineering: A Technology Review

In this paper, we review the technological bases of mobile health (mHealth). First, we derive a component-based mHealth architecture prototype from an Institute of Electrical and Electronics Engineers (IEEE)-based multistage research and filter process. Second, we analyze medical databases with regard to these prototypic mhealth system components.. We show the current state of research literature concerning portable devices with standard and additional equipment, data transmission technology, interface, operating systems and software embedment, internal and external memory, and power-supply issues. We also focus on synergy effects by combining different mHealth technologies (e.g., BT-LE combined with RFID link technology). Finally, we also make suggestions for future improvements in mHealth technology (e.g., data-protection issues, energy supply, data processing and storage)

Open-source automated external defibrillator

The Automated External Defibrillator (AED) is a medical device that analyzes a patient's electrocardiogram in order to establish whether he/she is suffering from the fatal condition of Sudden Cardiac Arrest (SCA), and subsequently allows the release of a therapeutic dose of electrical energy (i.e. defibrillation). SCA is responsible for over 300,000 deaths per year both in Europe and in USA, and immediate clinical assistance through defibrillation is fundamental for recovery. In this context, an open-source approach can easily lead in improvements to the distribution and efficiency of AEDs. The proposed Open-Source AED (OAED) is composed of two separate electric boards: a high voltage board (HV-B), which contains the circuitry required to perform defibrillation and a control board (C-B), which detects SCA in the patient and controls the HV-B. Computer simulations and preliminary tests show that the OAED can release a 200 J biphasic defibrillation in about 12 s and detects SCA with sensitivity higher than 90% and specificity of about 99%. The OAED was also conceived as a template and teaching tool in the framework of UBORA, a platform for design and sharing medical devices compliant to international standards

Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop.

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting

Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting

QRS COMPLEX DETECTION BASED ECG SIGNAL ARTEFACT DISCRIMINATION

Recent development in the area of wireless communications enabled advances in healthcare technology. Now, medical personnel are able to simultaneously observe electrocardiogram signals of large number of remote patients. The electrocardiogram signals are acquired by the patients themselves at their homes or other places far away from hospitals, and are transmitted almost instantly through modern communication systems. Unfortunately, the quality of the acquired signals is highly susceptible to artefacts. Here we outline a new algorithm dedicated to electrocardiograph telemetry systems which evaluates the quality of signals measured in unsupervised environments, rises the certainty of the produced diagnoses, and accelerates protective actions when necessary

Employment of artificial intelligence mechanisms for e-Health systems in order to obtain vital signs and detect diseases from medical images improving the processes of online consultations and diagnosis

Nowadays e-Health web applications allow doctors to access different types of features, such as knowing which medication the patient has consumed or performing online consultations. Internet systems for healthcare can be improved by using artificial intelligence mechanisms for the process of detecting diseases and obtaining biological data, allowing medical professionals to have important information that facilitates the diagnosis process and the choice of the correct treatment for each particular person. The proposed research work aims to present an innovative approach when compared to traditional platforms, by providing online vital signs in real time, access to a web stethoscope, to a medical image uploader that predicts if a certain disease is present, through deep learning methods, and also allows the visualization of all historical data of a patient. This dissertation has the objective of defending the concept of online consultations, providing complementary functionalities to the traditional methods for performing medical diagnoses through the use of software engineering practices. The process of obtaining vital signs was done via artificial intelligence using a computer camera as sensor. This methodology requires that the user is at a state of rest during the measurements. This investigation led to the conclusion that, in the future, many medical processes will most likely be done online, where this practice is considered extremely helpful for the analysis and treatment of contagious diseases, or cases that require constant monitoring.No quotidiano, as aplicações Web e-Saúde permitem aos médicos acesso a diferentes tipos de funcionalidades, como saber qual a medicação que o doente consumiu ou a realização de consultas online. Os sistemas via internet para a saúde podem ser melhorados, utilizando mecanismos de inteligência artificial para os processos de deteção de doenças e de obtenção de dados biológicos, permitindo que os médicos tenham informações importantes que facilitam o processo de diagnóstico ou a escolha do tratamento correto para um determinado utente. O trabalho de investigação proposto pretende apresentar uma abordagem inovadora na comparação com as plataformas tradicionais, ao disponibilizar sinais vitais online em tempo real, acesso a um estetoscópio web, a um uploader de imagens médicas que prevê se uma determinada doença está presente, através de métodos de aprendizagem profunda, bem como permite visualizar todos os dados históricos de um paciente. Esta dissertação visa defender o conceito de consultas virtuais, providenciando funcionalidades complementares aos processos tradicionais de realização de um diagnóstico médico, através da utilização de práticas de engenharia de software. O processo de obtenção de sinais vitais foi feito através de inteligência artificial para visão computacional utilizando uma câmara de computador. Esta metodologia requer que o utilizador esteja em estado de repouso durante a obtenção dos dados medidos. Esta investigação permitiu concluir que, no futuro, muitos processos médicos atuais provavelmente serão feitos online, sendo esta prática considerada extremamente útil na análise e tratamento de doenças contagiosas, ou de casos que requerem acompanhamento constante

Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector

Cardiovascular diseases (CVDs) are the number one cause of death worldwide. While there is growing evidence that the atrial fibrillation (AF) has strong associations with various CVDs, this heart arrhythmia is usually diagnosed using electrocardiography (ECG) which is a risk-free, non-intrusive, and cost-efficient tool. Continuously and remotely monitoring the subjects' ECG information unlocks the potentials of prompt pre-diagnosis and timely pre-treatment of AF before the development of any life-threatening conditions/diseases. Ultimately, the CVDs associated mortality could be reduced. In this manuscript, the design and implementation of a personalized healthcare system embodying a wearable ECG device, a mobile application, and a back-end server are presented. This system continuously monitors the users' ECG information to provide personalized health warnings/feedbacks. The users are able to communicate with their paired health advisors through this system for remote diagnoses, interventions, etc. The implemented wearable ECG devices have been evaluated and showed excellent intra-consistency (CVRMS=5.5%), acceptable inter-consistency (CVRMS=12.1%), and negligible RR-interval errors (ARE<1.4%). To boost the battery life of the wearable devices, a lossy compression schema utilizing the quasi-periodic feature of ECG signals to achieve compression was proposed. Compared to the recognized schemata, it outperformed the others in terms of compression efficiency and distortion, and achieved at least 2x of CR at a certain PRD or RMSE for ECG signals from the MIT-BIH database. To enable automated AF diagnosis/screening in the proposed system, a ResNet-based AF detector was developed. For the ECG records from the 2017 PhysioNet CinC challenge, this AF detector obtained an average testing F1=85.10% and a best testing F1=87.31%, outperforming the state-of-the-art

False alarm reduction in critical care

High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 PhysioNet/Computing in Cardiology Challenge provides a set of 1250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm. For retrospective analysis, we provided a further 30 seconds of data after the alarm was triggered. A total of 750 data segments were made available for training and 500 were held back for testing. Each alarm was reviewed by expert annotators, at least two of whom agreed that the alarm was either true or false. Challenge participants were invited to submit a complete, working algorithm to distinguish true from false alarms, and received a score based on their program's performance on the hidden test set. This score was based on the percentage of alarms correct, but with a penalty that weights the suppression of true alarms five times more heavily than acceptance of false alarms. We provided three example entries based on well-known, open source signal processing algorithms, to serve as a basis for comparison and as a starting point for participants to develop their own code. A total of 38 teams submitted a total of 215 entries in this year's Challenge. This editorial reviews the background issues for this challenge, the design of the challenge itself, the key achievements, and the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for future changes in the field of patient monitoring as a result of the Challenge.National Institutes of Health (U.S.) (Grant R01-GM104987)National Institute of General Medical Sciences (U.S.) (Grant U01-EB-008577)National Institutes of Health (U.S.) (Grant R01-EB-001659

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic