Advances in telemedicine technologies have offered clinicians greater levels of real-time guidance and technical assistance for diagnoses, monitoring, operations or interventions from colleagues based in remote locations. The topic includes the use of videoconferencing, mentorship during surgical procedures, or machine-to-machine communication to process data from one location by programmes running in another. This edited book presents a variety of technologies with applications in telemedicine, originating from the fields of biomedical sensors, wireless sensor networking, computer-aided diagnosis methods, signal and image processing and analysis, automation and control, virtual and augmented reality, multivariate analysis, and data acquisition devices. The Internet of Medical Things (IoMT), surgical robots, telemonitoring, and teleoperation systems are also explored, as well as the associated security and privacy concerns in this field. Topics covered include critical factors in the development, implementation and evaluation of telemedicine; surgical tele-mentoring; technologies in medical information processing; recent advances of signal/image processing techniques in healthcare; a real-time ECG processing platform for telemedicine applications; data mining in telemedicine; social work and telemental health services for rural and remote communities; applying telemedicine to social work practice and education; advanced telemedicine systems for remote healthcare monitoring; the impact of tone-mapping operators and viewing devices on visual quality of experience of colour and grey-scale HDR images; modelling the relationships between changes in EEG features and subjective quality of HDR images; IoMT and healthcare delivery in chronic diseases; and transform domain robust watermarking method using Riesz wavelet transform for medical data security and privacy.

Machine learning algorithms are increasingly finding applications in the healthcare sector. Whether assisting a clinician to process an individual patient's data or helping administrators view hospital bed turnover, the volume and complexity of healthcare data is a compelling reason for the development of machine learning based tools to aid in its interpretation and use.

This edited book focuses on the applications of machine learning in the healthcare sector, both at the macro-level for guiding policy decisions, and at the granular level, showing how ML techniques can be applied to process an individual patient's medical data to swiftly aid diagnosis.

Written by an international team of experts, the book presents several applications of machine learning in the healthcare sector, including health system planning, optimisation and preparedness, outlining the benefits and challenges of coordination and data sharing. Machine learning has many applications in processing patient data and topics such as arrhythmia detection, image-guided microsurgery and early detection of Alzheimer's disease are discussed in depth. The book also looks at machine learning applications exploiting wearable sensors for real-time analysis and concepts around enhancing physical performance.

Suitable for an audience of computer scientists, healthcare engineers and those involved with digital medicine, this book brings together a plethora of machine learning applications from across the board of the healthcare services.

The healthcare arena has seen a shift in recent years, with more healthcare provisions being delivered or managed via electronic means. Healthcare providers can now provide patients with diagnosis, treatment, monitoring, or a prescription without ever sharing the same physical space. With so much more patient data now stored and accessed electronically, the security of this information is ever more critical to the delivery of effective and efficient healthcare services. As blockchains are resistant to modification of their data, blockchain technology therefore provides traceable and reliable security to e-Healthcare systems and services.

Introducing the fundamentals of blockchain technology and discussing its applications in the e-Healthcare sphere, the editors highlight recent research and development in blockchain technology, specifically in healthcare environments such as e-healthcare records and data security, health insurance management and fraud detection, pharmaceutical supply chain management and drug traceability, and IoT enabled patient monitoring. Including a case study on managing e-Healthcare data the editors also explore the challenges and future directions of using blockchain technology in delivering and managing e-Healthcare provision.

Written by a range of international experts, the book will be of interest to researchers and academics in information security, data scientists, and healthcare professionals/administrators with responsibility for e-Healthcare records.

A secured system for Healthcare 4.0 is vital to all stakeholders, including patients and caregivers. Using the new Blockchain system of trusted ledgers would help guarantee authenticity in the multi-access system that is Healthcare 4.0. This is the first comprehensive book that explores how to achieve secure systems for Healthcare 4.0 using Blockchain, with emphasis on the key challenges of privacy and security. The book is organized into four sections. The first section is focused on 5G healthcare privacy and security concerns. The second section discusses healthcare architecture and emerging technologies. The third section covers the role of artificial intelligence for data security and privacy in 5G healthcare services. Finally, the last section systematically illustrates the adoption of blockchain in various applications of 5G healthcare. The book is essential reading for all involved in setting up, running, and maintaining healthcare information systems. Engineers, scientists, technologists, developers, designers, and researchers in healthcare technologies, health informatics, security, and information technology will find the content particularly useful.

While digital transformations are happening in all walks of society and business, there is real potential for improving the quality of life of the elderly using digital methods and tools. This book evolved from a recent multi-country and multi-disciplinary initiative called Digital Health for the Ageing Population. This project (2019-2021) aimed to promote the general awareness of digital health for the ageing population with collaborative research across several countries including Australia, Bangladesh, China, India, Japan, Norway, The Netherlands, and USA.Digital health promises to deliver better healthcare quality cost-efficiently to more people, especially in the case of lifestyle diseases such as diabetes. It will achieve this by combining the benefits of telehealth, eHealth, data-driven personalised healthcare, and evidence-based care. This book presents a discussion of evolving digital technologies, such as smart phones and assisted living, and innovative digitally based services that are helping improve the quality and cost of healthcare for the elderly.With its international scope and detailed coverage of relevant digital methods and tools, this book will benefit healthcare technologists, ICT developers, managers of healthcare and mobile healthcare projects, and academic researchers working in related fields.

Electroencephalography (EEG) is an electrophysiological monitoring method used to record the brain activity in brain-computer interface (BCI) systems. It records the electrical activity of the brain, is typically non-invasive with electrodes placed along the scalp, requires relatively simple and inexpensive equipment, and is easier to use than other methods. EEG-based BCI methods provide modest speed and accuracy which is why multichannel systems and proper signal processing methods are used for feature extraction, feature selection and feature classification to discriminate among several mental tasks. This edited book presents state of the art aspects of EEG signal processing methods, with an emphasis on advanced strategies, case studies, clinical practices and applications such as EEG for meditation, auditory selective attention, sleep apnoea; person authentication; handedness detection, Parkinson's disease, motor imagery, smart air travel support and brain signal classification.

No longer confined to medical devices, medical software has become a pervasive technology giving healthcare operators access to clinical information stored in electronic health records and clinical decision support systems, supporting robot-assisted telesurgery, and providing the technology behind ambient assisted living. These systems and software must be designed, built and maintained according to strict regulations and standards to ensure that they are safe, reliable and secure. Engineering High Quality Medical Software illustrates how to exploit techniques, methodologies, development processes and existing standards to realize high-confidence medical software. After an introductory survey of the topic the book covers global regulations and standards (including EU MDD 93/42/EEC, FDA Title 21 of US CFR, ISO 13485, ISO 14971, IEC 52304, IEEE 1012 and ISO/IEC 29119), verification and validation techniques and techniques, and methodologies and engineering tasks for the development, configuration and maintenance of medical software.

A major use of practical predictive analytics in medicine has been in the diagnosis of current diseases, particularly through medical imaging. Now there is sufficient improvement in AI, IoT and data analytics to deal with real time problems with an increased focus on early prediction using machine learning and deep learning algorithms. With the power of artificial intelligence alongside the internet of 'medical' things, these algorithms can input the characteristics/data of their patients and get predictions of future diagnoses, classifications, treatment and costs.

Evolving Predictive Analytics in Healthcare: New AI techniques for real-time interventions discusses deep learning algorithms in medical diagnosis, including applications such as Covid-19 detection, dementia detection, and predicting chemotherapy outcomes on breast cancer tumours. Smart healthcare monitoring frameworks using IoT with big data analytics are explored and the latest trends in predictive technology for solving real-time health care problems are examined. By using real-time data inputs to build predictive models, this new technology can literally 'see' your future health and allow clinicians to intervene as needed.

This book is suitable reading for researchers interested in healthcare technology, big data analytics, and artificial intelligence.

Ultrasound imaging technology has experienced a dramatic change in the last 30 years. Because of its non-invasive nature and continuing improvements in image quality, ultrasound imaging is progressively achieving an important role in the assessment and characterization of cardiovascular imaging. Speckle is inherent in ultrasound imaging giving rise to a granular appearance instead of homogeneous, flat shades of gray, as is visible and as such, speckle can severely compromise interpretation of ultrasound images, particularly in discrimination of small structures. On the other hand, speckle can be used in the detection of time varying phenomena, or tracking tissue motion. The objective of this book is to provide a reference edited volume covering the whole spectrum of speckle phenomena, theoretical background and modelling, algorithms and selected applications in cardiovascular ultrasound imaging and video processing and analysis. The book is organized under the following four parts, Part I: Introduction to Speckle Noise; Part II: Speckle Filtering; Part III: Speckle Tracking; Part IV: Selected Applications in Cardiovascular Imaging.

Interest in Information and Communication Technologies for human monitoring, smart health and assisted living is growing due to the significant impact that these technologies are expected to have on improving the quality of life of ageing populations around the world. This book brings together chapters written by a range of researchers working in these topics, providing an overview of the areas and covering current research, developments and applications for a readership of researchers and research-led engineering practitioners. It discusses the promises and the possible advantages of these technologies, and also indicates the challenges for the future. Topics covered include: personal monitoring and health data acquisition in smart homes; contactless monitoring of respiratory activity; technology-based assistance of people with dementia; wearable sensors for gesture analysis; design and prototyping of home automation systems for the monitoring of elderly people; multi-sensor platform for circadian rhythm analysis; smart multi-sensor solutions for activity detection; human monitoring based on heterogeneous sensor networks; mobile health for vital signs and gait monitoring systems; and smartphone-based blood pressure monitoring for falls risk assessment.