Mobile cloud-computing-based healthcare service by noncontact ECG monitoring

Unraveling the role of cloud computing in health care system and biomedical sciences

Cloud computing has emerged as a transformative force in healthcare and biomedical sciences, offering scalable, on-demand resources for managing vast amounts of data. This review explores the integration of cloud computing within these fields, highlighting its pivotal role in enhancing data management, security, and accessibility. We examine the application of cloud computing in various healthcare domains, including electronic medical records, telemedicine, and personalized patient care, as well as its impact on bioinformatics research, particularly in genomics, proteomics, and metabolomics. The review also addresses the challenges and ethical considerations associated with cloud-based healthcare solutions, such as data privacy and cybersecurity. By providing a comprehensive overview, we aim to assist readers in understanding the significance of cloud computing in modern medical applications and its potential to revolutionize both patient care and biomedical research.

SIMpLE: A Mobile Cloud-Based System for Health Monitoring of People with ALS

Adopting telemonitoring services during the pandemic for people affected by chronic disease is fundamental to ensure access to health care services avoiding the risk of COVID-19 infection. Among chronic diseases, Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig’s disease, is a progressive neurodegenerative disease of adulthood, caused by the loss of spinal, bulbar and cortical motor neurons, which leads to paralysis of the voluntary muscles and, also, involves respiratory ones. Therefore, remote monitoring and teleconsulting are essential services for ALS patients with limited mobility, as the disease progresses, and for those living far from ALS centres and hospitals. In addition, the COVID 19 pandemic has increased the need to remotely provide the best care to patients, avoiding infection during ALS centre visits. The paper illustrates an innovative, secure medical monitoring and teleconsultation mobile cloud-based system for disabled people, such as those with ALS (Amyotrophic Lateral Sclerosis). The design aims to remotely monitor biosignals, such as ECG (electrocardiographic) and EMG (electromyographic) signals of ALS patients in order to prevent complications related to the pathology.

A smart portable ECG monitoring system with high precision and low power consumption

In this paper, a smart low-power wearable ECG monitoring and analyzing system which has an advanced architecture and low power consumption has been developed. There is a single channel ECG monitor that removes all loose wires from the system and minimizes the footprint on the user, ECG data can be continuously monitored for up to 14 days without affecting daily life. In addition, Bluetooth low energy (BLE), smartphone APP, analysis software and management platform have been added to the system for remote monitoring and whole process of heart health management. Clinical test to compare our wearable monitor with the Holter recorder are conducted on 108 groups under different ambulatory conditions. The results confirmed that it is equivalent to Holter in the clinical diagnosis of arrhythmia. This system can free patients from the short-range monitoring with lead wire entanglement to realize the long-term detection in daily life, early detection and early intervention; more convenient connections between patients and doctors, efficient use of medical resources, remote monitoring to reduce the burden of the medical and health system.

El-Bakry H, Abdelrazek S. Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.

End-To-End Deep Learning Framework for Coronavirus (COVID-19) Detection and Monitoring

Coronavirus (COVID-19) is a new virus of viral pneumonia. It can outbreak in the world through person-to-person transmission. Although several medical companies provide cooperative monitoring healthcare systems, these solutions lack offering of the end-to-end management of the disease. The main objective of the proposed framework is to bridge the current gap between current technologies and healthcare systems. The wireless body area network, cloud computing, fog computing, and clinical decision support system are integrated to provide a comprehensive and complete model for disease detection and monitoring. By monitoring a person with COVID-19 in real time, physicians can guide patients with the right decisions. The proposed framework has three main layers (i.e., a patient layer, cloud layer, and hospital layer). In the patient layer, the patient is tracked through a set of wearable sensors and a mobile app. In the cloud layer, a fog network architecture is proposed to solve the issues of storage and data transmission. In the hospital layer, we propose a convolutional neural network-based deep learning model for COVID-19 detection based on patient’s X-ray scan images and transfer learning. The proposed model achieved promising results compared to the state-of-the art (i.e., accuracy of 97.95% and specificity of 98.85%). Our framework is a useful application, through which we expect significant effects on COVID-19 proliferation and considerable lowering in healthcare expenses.

Tracking Knowledge Evolution in Cloud Health Care Research: Knowledge Map and Common Word Analysis

Background

With the continuous development of the internet and the explosive growth in data, big data technology has emerged. With its ongoing development and application, cloud computing technology provides better data storage and analysis. The development of cloud health care provides a more convenient and effective solution for health. Studying the evolution of knowledge and research hotspots in the field of cloud health care is increasingly important for medical informatics. Scholars in the medical informatics community need to understand the extent of the evolution of and possible trends in cloud health care research to inform their future research.

Objective

Drawing on the cloud health care literature, this study aimed to describe the development and evolution of research themes in cloud health care through a knowledge map and common word analysis.

Methods

A total of 2878 articles about cloud health care was retrieved from the Web of Science database. We used cybermetrics to analyze and visualize the keywords in these articles. We created a knowledge map to show the evolution of cloud health care research. We used co-word analysis to identify the hotspots and their evolution in cloud health care research.

Results

The evolution and development of cloud health care services are described. In 2007-2009 (Phase I), most scholars used cloud computing in the medical field mainly to reduce costs, and grid computing and cloud computing were the primary technologies. In 2010-2012 (Phase II), the security of cloud systems became of interest to scholars. In 2013-2015 (Phase III), medical informatization enabled big data for health services. In 2016-2017 (Phase IV), machine learning and mobile technologies were introduced to the medical field.

Conclusions

Cloud health care research has been rapidly developing worldwide, and technologies used in cloud health research are simultaneously diverging and becoming smarter. Cloud–based mobile health, cloud–based smart health, and the security of cloud health data and systems are three possible trends in the future development of the cloud health care field.

An Edge Computing Based Smart Healthcare Framework for Resource Management

The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time.

Noncontact Wearable Wireless ECG Systems for Long-Term Monitoring

Electrocardiography (ECG) is the most common and extensively used vital sign monitoring method in modern healthcare systems. Different designs of ambulatory ECG systems were developed as alternatives to the commonly used 12-lead clinical ECG systems. These designs primarily focus on portability and user convenience, while maintaining signal integrity and lowering power consumption. Here, a wireless ECG monitoring system is developed using flexible and dry capacitive electrodes for long-term monitoring of cardiovascular health. Our capacitive-coupled dry electrodes can measure ECG signals over a textile-based interface material between the skin and electrodes. The electrodes are connected to a data acquisition system that receives the raw ECG signals from the electrodes and transmits the data using Bluetooth to a computer. A software application was developed to process, store, and display the ECG signal in real time. ECG measurements were obtained over different types of textile materials and in the presence of body movements. Our experimental results show that the performance of our ECG system is comparable to other reported ECG monitoring systems. In addition, to put this research into perspective, recent ambulatory ECG monitoring systems, ECG systems-on-chip, commercial ECG monitoring systems, and different state-of-the-art ECG systems are reviewed, compared, and critically discussed.

Fog computing architectures for healthcare

Purpose

The purpose of this study is to analyze how embedding of self-powered wireless sensors into cloud computing further enables such a system to become a sustainable part of work environment.

Design/methodology/approach

This is exemplified by an application scenario in healthcare that was developed in the context of the OpSIT project in Germany. A clearly outlined three-layer architecture, in the sense of Internet of Things, is presented. It provides the basis for integrating a broad range of sensors into smart healthcare infrastructure. More specifically, by making use of short-range communication sensors (sensing layer), gateways which implement data transmission and low-level computation (fog layer) and cloud computing for processing the data (application layer).

Findings

A technical in-depth analysis of the first two layers of the infrastructure is given to prove reliability and to determine the communication quality and availability in real-world scenarios. Furthermore, two example use-cases that directly apply to a healthcare environment are examined, concluding with the feasibility of the presented approach.

Practical implications

Finally, the next research steps, oriented towards the semantic tagging and classification of data received from sensors, and the usage of advanced artificial intelligence-based algorithms on this information to produce useful knowledge, are described together with the derived social benefits.

Originality/value

The work presents an innovative, extensible and scalable system, proven to be useful in healthcare environments.

A low-power and miniaturized electrocardiograph data collection system with smart textile electrodes for monitoring of cardiac function

With the increasing aging population as well as health concerns, chronic heart disease has become the focus of public attention. A comfortable, low-powered, and wearable electrocardiogram (ECG) system for continuously monitoring the elderly's ECG signals over several hours is important for preventing cardiovascular diseases. Traditional ECG monitoring apparatus is often inconvenient to carry, has many electrodes to attach to the chest, and has a high-power consumption. There is also a challenge to design an electrocardiograph that satisfies requirements such as comfort, confinement, and compactness. Based on these considerations, this study presents a biosensor acquisition system for wearable, ubiquitous healthcare applications using three textile electrodes and a recording circuit specialized for ECG monitoring. In addition, several methods were adopted to reduce the power consumption of the device. The proposed system is composed of three parts: (1) an ECG analog front end (AFE), (2) digital signal processing and micro-control circuits, and (3) system software. Digital filter methods were used to eliminate the baseline wander, skin contact noise, and other interfering signals. A comparative study was conducted using this system to observe its performance with two commercial Holter monitors. The experimental results demonstrated that the total power consumption of this proposed system in a full round of ECG acquisition was only 29.74 mW. In addition, this low-power system performed well and stably measured the heart rate with an accuracy of 98.55 %. It can also contain a real-time dynamic display with organic light-emitting diodes (OLED) and wirelessly transmit information via a Bluetooth 4.0 module.

ECG by mobile technologies

Mobile electrocardiographs consist of three components: a mobile device (e.g. a smartphone), an electrocardiographic device or accessory, and a mobile application. Mobile platforms are small computers with sufficient computational power, good quality display, suitable data storage, and several possibilities of data transmission. Electrocardiographic electrodes and sensors for mobile use utilize unconventional materials, e.g. rubber, e-textile, and inkjet-printed nanoparticle electrodes. Mobile devices can be handheld, worn as vests or T-shirts, or attached to patient's skin as biopatches. Mobile electrocardiographic devices and accessories may additionally record other signals including respiratory rate, activity level, and geolocation. Large-scale clinical studies that utilize electrocardiography are easier to conduct using mobile technologies and the collected data are suitable for "big data" processing. This is expected to reveal phenomena so far inaccessible by standard electrocardiographic techniques.

Reduction of Motion Artifacts and Improvement of R Peak Detecting Accuracy Using Adjacent Non-Intrusive ECG Sensors

Non-intrusive electrocardiogram (ECG) monitoring has many advantages: easy to measure and apply in daily life. However, motion noise in the measured signal is the major problem of non-intrusive measurement. This paper proposes a method to reduce the noise and to detect the R peaks of ECG in a stable manner in a sitting arrangement using non-intrusive sensors. The method utilizes two capacitive ECG sensors (cECGs) to measure ECG, and another two cECGs located adjacent to the sensors for ECG are added to obtain the information on motion. Then, active noise cancellation technique and the motion information are used to reduce motion noise. To verify the proposed method, ECG was measured indoors and during driving, and the accuracy of the detected R peaks was compared. After applying the method, the sum of sensitivity and positive predictivity increased 8.39% on average and 26.26% maximally in the data. Based on the results, it was confirmed that the motion noise was reduced and that more reliable R peak positions could be obtained by the proposed method. The robustness of the new ECG measurement method will elicit benefits to various health care systems that require noninvasive heart rate or heart rate variability measurements.

Community-Based ECG Monitoring System for Patients with Cardiovascular Diseases

This study aims to develop a community-based electrocardiogram (ECG) monitoring system for cardiac outpatients to wirelessly detect heart rate, provide personalized healthcare, and enhance interactive social contact because of the prevalence of deaths from cardiovascular disease and the growing problem of aging in the world. The system not only strengthens the performance of the ECG monitoring system but also emphasizes the ergonomic design of wearable devices and user interfaces. In addition, it enables medical professionals to diagnose cardiac symptoms remotely and electronically manage medical reports and suggestions. The experimental result shows high performance of the dry electrode, even in dynamic conditions. The comparison result with different ECG healthcare systems shows the essential factors that the system should possess and the capability of the proposed system. Finally, a user survey was conducted based on the unified theory of acceptance and users of technology (UTAUT) model.

A Wearable Context-Aware ECG Monitoring System Integrated with Built-in Kinematic Sensors of the Smartphone

Continuously monitoring the ECG signals over hours combined with activity status is very important for preventing cardiovascular diseases. A traditional ECG holter is often inconvenient to carry because it has many electrodes attached to the chest and because it is heavy. This work proposes a wearable, low power context-aware ECG monitoring system integrated built-in kinetic sensors of the smartphone with a self-designed ECG sensor. The wearable ECG sensor is comprised of a fully integrated analog front-end (AFE), a commercial micro control unit (MCU), a secure digital (SD) card, and a Bluetooth module. The whole sensor is very small with a size of only 58 × 50 × 10 mm for wearable monitoring application due to the AFE design, and the total power dissipation in a full round of ECG acquisition is only 12.5 mW. With the help of built-in kinetic sensors of the smartphone, the proposed system can compute and recognize user’s physical activity, and thus provide context-aware information for the continuous ECG monitoring. The experimental results demonstrated the performance of proposed system in improving diagnosis accuracy for arrhythmias and identifying the most common abnormal ECG patterns in different activities. In conclusion, we provide a wearable, accurate and energy-efficient system for long-term and context-aware ECG monitoring without any extra cost on kinetic sensor design but with the help of the widespread smartphone.

Architecture Design of Healthcare Software-as-a-Service Platform for Cloud-Based Clinical Decision Support Service

Objectives

To design a cloud computing-based Healthcare Software-as-a-Service (SaaS) Platform (HSP) for delivering healthcare information services with low cost, high clinical value, and high usability.

Methods

We analyzed the architecture requirements of an HSP, including the interface, business services, cloud SaaS, quality attributes, privacy and security, and multi-lingual capacity. For cloud-based SaaS services, we focused on Clinical Decision Service (CDS) content services, basic functional services, and mobile services. Microsoft's Azure cloud computing for Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) was used.

Results

The functional and software views of an HSP were designed in a layered architecture. External systems can be interfaced with the HSP using SOAP and REST/JSON. The multi-tenancy model of the HSP was designed as a shared database, with a separate schema for each tenant through a single application, although healthcare data can be physically located on a cloud or in a hospital, depending on regulations. The CDS services were categorized into rule-based services for medications, alert registration services, and knowledge services.

Conclusions

We expect that cloud-based HSPs will allow small and mid-sized hospitals, in addition to large-sized hospitals, to adopt information infrastructures and health information technology with low system operation and maintenance costs.

A scoping review of cloud computing in healthcare

BACKGROUND

Cloud computing is a recent and fast growing area of development in healthcare. Ubiquitous, on-demand access to virtually endless resources in combination with a pay-per-use model allow for new ways of developing, delivering and using services. Cloud computing is often used in an "OMICS-context", e.g. for computing in genomics, proteomics and molecular medicine, while other field of application still seem to be underrepresented. Thus, the objective of this scoping review was to identify the current state and hot topics in research on cloud computing in healthcare beyond this traditional domain.

METHODS

MEDLINE was searched in July 2013 and in December 2014 for publications containing the terms "cloud computing" and "cloud-based". Each journal and conference article was categorized and summarized independently by two researchers who consolidated their findings.

RESULTS

102 publications have been analyzed and 6 main topics have been found: telemedicine/teleconsultation, medical imaging, public health and patient self-management, hospital management and information systems, therapy, and secondary use of data. Commonly used features are broad network access for sharing and accessing data and rapid elasticity to dynamically adapt to computing demands. Eight articles favor the pay-for-use characteristics of cloud-based services avoiding upfront investments. Nevertheless, while 22 articles present very general potentials of cloud computing in the medical domain and 66 articles describe conceptual or prototypic projects, only 14 articles report from successful implementations. Further, in many articles cloud computing is seen as an analogy to internet-/web-based data sharing and the characteristics of the particular cloud computing approach are unfortunately not really illustrated.

CONCLUSIONS

Even though cloud computing in healthcare is of growing interest only few successful implementations yet exist and many papers just use the term "cloud" synonymously for "using virtual machines" or "web-based" with no described benefit of the cloud paradigm. The biggest threat to the adoption in the healthcare domain is caused by involving external cloud partners: many issues of data safety and security are still to be solved. Until then, cloud computing is favored more for singular, individual features such as elasticity, pay-per-use and broad network access, rather than as cloud paradigm on its own.

Barriers to pilot mobile teleophthalmology in a rural hospital in Southern Malawi

INTRODUCTION

Malawi has one of the highest HIV prevalences in Sub-Saharan Africa. The rate of eligible HIV-infected people being initiated on antiretroviral therapy (ART) and retained in HIV-care is currently far from adequate. Consequently, many people continue present with advanced immunosuppression at public health facilities, often with undiagnosed opportunistic infections (OIs).

METHODS

In this context, mHealth was the innovation chosen to assist Eye Clinical Officers in early diagnosis of HIV-related diseases having eye manifestations in a rural hospital in Thyolo, Southern Malawi.

RESULTS

The mTeleophthalmology program began in October 2013, but was stopped prematurely due to organizational and technological barriers that compromised its feasibility.

CONCLUSION

Sharing these barriers might be useful to inform the design of similar innovations in other resource-limited settings with a high HIV prevalence and a dearth of eye specialists with capacity to diagnose HIV-related retinopathies.

A mobile device system for early warning of ECG anomalies

With the rapid increase in computational power of mobile devices the amount of ambient intelligence-based smart environment systems has increased greatly in recent years. A proposition of such a solution is described in this paper, namely real time monitoring of an electrocardiogram (ECG) signal during everyday activities for identification of life threatening situations. The paper, being both research and review, describes previous work of the authors, current state of the art in the context of the authors' work and the proposed aforementioned system. Although parts of the solution were described in earlier publications of the authors, the whole concept is presented completely for the first time along with the prototype implementation on mobile device—a Windows 8 tablet with Modern UI. The system has three main purposes. The first goal is the detection of sudden rapid cardiac malfunctions and informing the people in the patient's surroundings, family and friends and the nearest emergency station about the deteriorating health of the monitored person. The second goal is a monitoring of ECG signals under non-clinical conditions to detect anomalies that are typically not found during diagnostic tests. The third goal is to register and analyze repeatable, long-term disturbances in the regular signal and finding their patterns.