Healthcare Technologies

Breathomics

Mon, 12 Oct 2026 14:22:14 GMT

By analysing the composition of the volatile organic compounds (VOCs) in exhaled breath, medical professionals can gain insight to metabolic processes, which can reflect various physiological or pathological processes and potential health considerations such as the presence of certain diseases or other conditions. Breathomics is a useful non-invasive technique whose applications are being explored in assessing respiratory diseases, cancer detection, and monitoring the effects of environmental exposures.

This book offers a comprehensive and interdisciplinary exploration of breathomics sensors, encompassing their fundamental scientific principles, technological innovations, and diverse clinical applications. It examines the potential of volatile organic compounds (VOCs) in exhaled breath as non-invasive biomarkers for the early detection, monitoring, and management of a broad spectrum of diseases, including diabetes, respiratory illnesses, gastrointestinal and hepatic disorders, and various cancers.

The authors discuss sensor technologies and the role of AL/ML algorithms to enhance sensor sensitivity, specificity, and real-time diagnostic capabilities, thereby facilitating the advancement of precision medicine. Particular emphasis is placed on real-world clinical case studies that showcase sensor performance across various diagnostic scenarios, alongside strategic analyses of current technological challenges. Furthermore, the text underscores future innovations, scalability considerations, and translational potential within the field.

Breathomics: Volatile organic compounds (VOCs) sensor technology for healthcare diagnostics is intended for an audience of researchers, clinicians, and engineers in biomedical engineering, diagnostic technologies, and clinical medicine, with a particular interest in breathomics, metabolomics, biosensors, and non-invasive diagnostics.

Multichannel Heart Sound Signal Processing and Classification

Tue, 08 Sep 2026 08:24:17 GMT

Heart disease, including coronary artery disease (CAD) and valvular heart disease (VHD), is the leading cause of mortality and morbidity in the world. Early diagnosis is important to prevent further development of the disease. Whilst standard diagnosis methods can be highly costly and invasive, by analysing heart murmur sounds via a phonocardiogram (PCG), patterns in the heart sound characteristics can be readily detected. The intensity, timing, and duration of the heart's sounds can correspond to murmurs, valvular issues, or congenital heart defects. Sounds are recorded using electronic stethoscopes to detect abnormalities not audible to the human ear. This technology is unique in its ability to be deployed in remote and underserved areas to aid in the timely diagnosis of heart diseases.

Multichannel Heart Sound Signal Processing and Classification: A concise overview explores advanced heart sound signal processing techniques, covering signal acquisition, pre-processing, segmentation, feature selection, and classification.

Covering practical wearable systems and machine learning, multichannel signal processing, and feature extraction methods for improving non-invasive, affordable diagnosis of various heart diseases and congenital heart defects, the book is suitable for an audience of scientists, researchers, physicians, biomedical and electronics engineers, and those working in the medical technology industry to broaden their perspective on heart sound signal processing.

Deep Learning Models in Medical Imaging

Tue, 08 Sep 2026 08:21:54 GMT

As healthcare technology moves towards a near-human level of performance in image recognition for screening, diagnosis, staging, and prognosis, this authored book lays the groundwork for understanding AI's transformative impact on the analysis of medical images.

The book introduces fundamental AI concepts - machine learning and deep learning - and outlines how they are applied in diagnostic automation. Readers are introduced to common imaging techniques like X-ray, MRI, CT, and ultrasound, as well as their integration into clinical settings. Historical milestones, current technologies, and deployment challenges are discussed alongside the associated legal, ethical, and bias-related issues.

The book covers the complete pipeline from image acquisition to model deployment in clinical settings. Special focus is given to lightweight, explainable AI (XAI) systems suitable for resource-constrained healthcare environments.

The authors explore diagnostic applications such as diabetic retinopathy screening using fundus images, skin lesion classification, interpretation of X-ray images, mammography, and brain scans. Novel techniques including federated learning, self-supervised models, and domain adaptation for diagnostic robustness are thoroughly explored, highlighting the global significance of AI-driven healthcare.

Deep Learning Models in Medical Imaging: Diagnostic innovations and clinical intelligence is suitable for an audience of AI researchers in healthcare, biomedical engineers, diagnostic staff and others working in medical imaging and data science.

Telemedicine Systems for Unreached Communities

Tue, 08 Sep 2026 08:19:12 GMT

Telemedicine - broadly defined as the use of electronic information and communication technologies to deliver healthcare services at a distance - offers an opportunity to create more equitable healthcare provision for unreached communities. Through the integration of telecommunications technologies, remote diagnostics, electronic health systems, mobile health platforms, and virtual consultation models, telemedicine enables healthcare providers to deliver clinical services across significant geographic and socioeconomic divides.

Successful telemedicine systems are shaped by a broad range of interdependent factors including policy environments, digital infrastructure, healthcare workforce capacity, cultural contexts, institutional readiness, governance frameworks, economic sustainability, and community trust. This book is an attempt to learn from both the successes and the failed attempts to deploy telemedicine systems. It brings together clinicians, technologists, public health professionals, health equity researchers, and community advocates who have worked on the frontlines of telemedicine implementation in settings where the need is greatest and the obstacles are most formidable.

The authors offer up a multidisciplinary exploration of telemedicine implementation focusing particularly on the operational, ethical, infrastructural, organizational, and social dimensions associated with delivering healthcare in low-resource settings. Their collective testimony drawn from Pacific Oceania, East Africa, South Asia, Latin America, and the margins of wealthy nations constitutes a rigorous, field-tested account of what it takes to make telemedicine work for populations that standard health systems have left behind.

Telemedicine Systems for Unreached Communities: Context, challenges and opportunities is intended for practitioners and programme managers who are working in the field, for researchers and academics who study health systems and health equity, and for the policymakers, funders and technology innovators who make decisions about where healthcare resources flow and how systems are designed.

Cryptography for Intelligent Healthcare Systems

Wed, 08 Jul 2026 10:11:22 GMT

Intelligent healthcare systems refer to the use of technologies, such as IoT, big data, AI, and machine learning, to improve the quality and efficiency of healthcare delivery. Healthcare data is both valuable and sensitive, and patients' data is increasingly at risk from cyber attacks on medical devices and critical healthcare infrastructure, posing serious risks to patient health and safety or compromising sensitive user data. As healthcare provision relies upon numerous interconnected systems, to facilitate the diagnosis or treatment of patients' health conditions, these systems rely on advanced cryptography to secure and protect sensitive user information.

This edited book in two volumes covers advanced cryptography for intelligent healthcare systems, examining robust cryptography-empowered frameworks and considering external threats, inside vulnerabilities, well-defined risks, and the resilience and robustness of the security architecture.

Volume 1 addresses foundations and emerging architectures, including legacy and modern cryptography, advanced cryptographic frameworks, including examples utilising AI, and explores the security of emerging systems and environments such as IoT and the internet-of-medical-things, 6G networks and multi-network emergency communication systems.

Volume 2 focuses on privacy, governance and deployment. Chapters cover device-class mappings, protocol profiles and deployment playbooks; FHIR-based systems using authenticated encryption, privacy-preserving federated learning and audit-grade integrity for intelligent matching systems; zkSNARK benchmark frameworks; ethical concerns and governance challenges, ethical-by-design cryptography; patient data autonomy; and security beyond cryptography.

Including case studies, highlighting critical lessons and design recommendations, these volumes provides researchers and academics with new insights into the real-world scenarios of the design, development, deployment, application, and management of cryptographic algorithms in modern healthcare systems.

Cryptography for Intelligent Healthcare Systems

Wed, 08 Jul 2026 10:13:45 GMT

Health Informatics

Fri, 12 Sep 2025 14:38:58 GMT

Health Informatics: Technologies and applications covers technological advances in healthcare that contribute to precision medicine and early detection of diseases. The editors explore the evolution of medical devices with attention to data management, patient safety and cost effectiveness.

Discussing health data and big data analytics for healthcare information, the editors also look at the application of artificial intelligence in the healthcare arena, examining the concepts of machine learning for image sensing and the importance of feature selection, class imbalance, model robustness, and scalability.

With the advancement of telemedicine, the book will also examine cloud computing systems and the internet of medical things (IoMT), with particular reference to cybersecurity concerns and the reliable management of electronic medical records.

The editors look at deploying these technologies for improved detection and therapeutic considerations for neurological and physiological conditions.

The book will be suitable for an audience of computer scientists and engineers particularly researchers working in healthcare technologies, AI/ML, computer science, data analysis or IoMT.

Introduction to Biomechatronics, 2nd Edition

Thu, 17 Jul 2025 13:54:41 GMT

Introduction to Biomechatronics, Second Edition, combines fundamental mechatronic (mechanics, electronics, robotics) engineering knowledge with state-of-the-art device designs that improve quality of life for patients worldwide. This new edition is comprehensively updated and includes new chapters on brain-machine interfaces and exoskeletons.

These volumes bring together mechanics and electronics with human systems, showing how technology can interact with human muscle, skeleton, and nervous systems to assist or replace limbs, senses, and even organs damaged by trauma, birth defects, or diseases.

Volume 1: Mechatronic considerations provides the engineering background to understand all the components of a biomechatronic system: the human subject, stimulus or actuation, transducers and sensors, signal conditioning elements, and feedback and control systems.

Volume 2: Systems and applications discusses devices used with specific functional systems of the body to which biomechatronics can be applied including: the nervous, respiratory, cardiovascular, and musculoskeletal systems, examining historical perspectives, technical advances and engineering analysis of current technological solutions such as developing prosthetic limbs or aids for locomotion, hearing, vision, and the cardiovascular system.

Introduction to Biomechatronics, Second Edition is essential reading for researchers and students in biomedical, mechanical, mechatronic and electrical engineers and those in related fields of robotics, sensors, computer science or healthcare systems device design.

Introduction to Biomechatronics, 2nd Edition

Thu, 17 Jul 2025 13:55:42 GMT

AI, Numerical Optimization, IoT and Blockchain for Healthcare 4.0

Thu, 17 Jul 2025 13:53:21 GMT

The healthcare sphere is becoming more interconnected, intelligent and data driven. The management of healthcare data - and developments in the devices, technologies and systems used to obtain, interpret, store and access it - is a key theme for many researchers in healthcare and technology fields. Sensors and IoT devices are increasingly used to obtain better information about patients' health, machine learning and numerical optimization techniques are being deployed in interpreting medical information, and secure healthcare records and telemedicine approaches are needed to enable clinicians and patients to access data remotely. All these technological advances fall under the umbrella term of Healthcare 4.0.

This book explores the ideas and practices around utilizing IoT integration within the healthcare ecosystem to improve patient diagnosis, monitoring, and treatment, using machine learning and optimization to develop proactive healthcare systems. The book aims to provide both the necessary theoretical foundations for a sound understanding of the topic, and experimental case studies to show how Healthcare 4.0 applies to real-world situations. Coverage includes data modelling, information discovery, prediction, smart healthcare, transparency in governance, and auditing.

The book's editors have brought together an international team of experts to discuss and share their opinions on Healthcare 4.0, evaluate existing solutions and provide real-world, practical case studies. Their intention has been to provide an interesting resource for researchers in the field of computer science, computer engineering, healthcare technology, computer vision, pattern recognition, machine learning, IoT, AI, signal processing, blockchain and big data and those in related disciplines.

Energy Harvesting Solutions for Implantable Medical Devices

Thu, 17 Jul 2025 13:52:26 GMT

Implantable devices are becoming more prevalent in the monitoring and treatment of patients with chronic diseases such as heart failure, diabetes and cancer. Conventional implantable devices are battery-powered, but these batteries can suffer from a short lifespan, bulky size, or leakage hazards. Energy harvesting technologies are therefore emerging as an alternative to battery-powered devices.

This book details the current availability of implantable devices with energy harvesting technologies used in modern therapy and treatment, and how to manage and control the quality and risk during design, manufacture, and validation. With chapters on kinetic energy, thermal energy, photovoltaic energy, biofuel energy, RF energy and wireless power transfer in implantable applications, the authors demonstrate how these technologies can harvest sufficient energy from the host human body which can be used to power implantable devices. Energy harvesting in different modern biomedical implantable applications is discussed and illustrated with examples which outline the benefits and drawbacks of energy harvesters used to power implantable devices.

Energy Harvesting Solutions for Implantable Medical Devices: Design, integration, and application of self-powered biomedical implants provides a useful overview of these developing technologies for an audience of biomedical engineers, researchers, clinicians and advanced students.

Techniques and Technologies in Electrical Stimulation for Neuromuscular Rehabilitation

Tue, 15 Oct 2024 10:36:56 GMT

In its simplest form, electrical stimulation is the application of electrical impulses to nerves via electrodes placed over the nerve or muscle or implanted within the body. The aim is to evoke a muscle contraction. People may not be able to activate their own muscles sufficiently to execute effective movement due to damage to the nervous system preventing the signals from the brain reaching the muscles, for example, after a stroke or spinal cord injury, or due to disuse, often because of pain. Electrical stimulation can be used to restore or improve impaired function by initiating or complementing muscle activity. Stimulation can be used either to provide exercise and so improve strength and endurance or timed to a physical activity such as walking to improve quality of movement and function.

Over the last twenty-five years electrical stimulation has moved from a research technique to an evidence-based clinical modality. Early applications were limited to the treatment of drop-foot and loss of upper limb function. However, advances in technology, understanding of neural recovery and clinical evidence have opened applications to treat a wide range of conditions, such as pressure sores, bladder, bowel and sexual dysfunction, spasticity and lower motor neuron damage. The reader is taken from the history of therapeutic electrical stimulation, through the physiology that underpins its use, to practical guidance in clinical applications and the regulatory issues that need to be considered in the development of new technologies. It presents the research evidence for each application, reflects on new technologies and applications, such as the use of afferent stimulation to increase central and peripheral neural excitability, and provides practical guidance for clinical use.

Techniques and Technologies in Electrical Stimulation for Neuromuscular Rehabilitation brings together experts from the fields of neuroscience, biomedical engineering and clinical research and practice. The non-technical style enables it to bridge the gap between disciplines, making it essential reading for clinicians, researchers, engineers and industrial developers specialising in electrical stimulation technologies. It aims to improve patient access to evidence-based interventions.

Secure Big-data Analytics for Emerging Healthcare in 5G and Beyond

Thu, 01 Aug 2024 13:07:04 GMT

Healthcare systems today are increasingly reliant on data gathered from multiple hospital systems, patient records or IoT devices. As more information is gathered, there is a need to ensure the data is kept and used securely. This edited book looks at secure big data analytics for healthcare and how the wealth of information is disseminated through open wireless channels to provide seamless coverage so that people can access and analyse the results obtained and intelligently manage and respond to a patient's needs.

The editors cover current and emerging frameworks, architectures, and solutions that address the requirements of secure big data analytics for the healthcare industry. The book also addresses the challenges of deploying security-based healthcare analytics for massive BDA (big data analytics) applications, through smart optimized network communication infrastructures, dense connectivity, and AI-driven models.

Topics include big data analytics, trustworthy data sharing, security challenges and privacy preserving techniques, authentication and access control schemes, deep learning models, risk modelling, and blockchain integration. The book provides a great reference for researchers in academia, network professionals, healthcare industry professionals, and researchers working towards emerging secure BDA solutions in 5G and beyond networks.

Exploring Intelligent Healthcare with Quantum Computing

Tue, 11 Jun 2024 14:53:25 GMT

Classical computers encode information in binary bits that can either be 0s or 1s. In a quantum computer, the basic unit of memory is a quantum bit or qubit. These qubits play a similar role in terms of storing information, but use physical systems, such as the spin of an electron or the orientation of a photon, to do so. In situations where there are a large number of possible feature combinations, quantum computers can consider them simultaneously, speeding up the data processing time.

In healthcare, where there are often large numbers of possible factors to consider, quantum computers can address them simultaneously, thereby allowing doctors to compare much, much more data, and all permutations of that data, in parallel to discover the best patterns that describe it, and therefore predict the best treatment options.

This edited book explores the field of quantum computing and machine learning for medical data processing, and is a useful resource for computer engineers, researchers, healthcare technologists and scientists specialising in quantum computing, quantum AI, data processing, deep learning, machine learning, smart healthcare, and medical data systems.

Cybersecurity in Emerging Healthcare Systems

Fri, 10 May 2024 10:30:51 GMT

Emerging healthcare networks are interconnected physical systems that use cyber technologies for interaction and functionality. The proliferation of massive internet-of-things (IoT) devices enables remote and distributed access to cutting-edge diagnostics and treatment options in modern healthcare systems. New security vulnerabilities are emerging due to the increasing complexity of the healthcare architecture, in particular, threats to medical devices and critical infrastructure pose significant concerns owing to their potential risks to patient health and safety. In recent times, patients have been exposed to high risks from attacks capable of disrupting critical medical infrastructure, communications facilities, and services, interfering with medical devices, or compromising sensitive user data.

This book seeks to present cyber risk and vulnerability models, considering a number of threats and examining how effective regulations could help guarantee medical device fidelity and trust. The book discusses the application of artificial intelligence and machine learning to provide practical learning-based solutions to address cyberattacks in emerging healthcare systems. The book focuses on the technical considerations, potential opportunities, critical cybersecurity challenges, the prospects and potential benefits of cybersecurity in emerging healthcare systems. Finally, the book presents case studies, highlighting critical lessons, and providing recommendations for designing AI-based cybersecurity architectures for emerging healthcare systems.

Written by an international team of authors, this book is suitable for an audience of industry-based and academic researchers, scientists, and computer engineers working in data science, cybersecurity and wireless communications particularly those specialising in healthcare data science and those in related fields.

Affective Computing Applications using Artificial Intelligence in Healthcare

Wed, 13 Mar 2024 10:45:49 GMT

Affective computing is the study and development of systems and devices that can recognise human emotions. This can be done using sensing technologies and AI algorithms to process biological signals or facial images to identify the different affective states, such as happiness, anger, fear, surprise, sadness and disgust. This non-invasive technique has applications in healthcare such as emotional impairment detection, mental health assessment, emotional stress assessment, cognitive decline detection, attention deficit disorders, neurodegenerative diseases, neurological disorders, autism spectrum disorder, stress, anxiety or other behavioural assessment.

This edited book provides an overview of the ongoing research on affective computing applications in healthcare using AI and IoT. This book covers recent advancements in computing technology, modelling methods, frameworks, and algorithms used for human affect detection using bio-signal and image processing methods.

The book explores the use of EEG signals, thermal imaging, eye-movement patterns, gesture recognition systems and IoT systems to gather information and discusses the use of deep learning, CNN and RNN-LSTM models of how this information can be usefully processed to detect emotional states.

Discussing the latest trends and developments in research in the field, this book is a useful resource for researchers in affective computing, affective neuroscience, cognitive neuroscience, computer vision, signal and image processing, cybersecurity, AI/ML/DL, data science, HCI, sensing and robotics.

Practicing physicians, clinical experts or researchers in experimental and applied cognitive psychology who wish to understand the emotional wellbeing (pain and stress level analysis) of patients may also find this book of interest.

Artificial Intelligence and Blockchain Technology in Modern Telehealth Systems

Thu, 07 Sep 2023 09:22:26 GMT

The expansion of telehealth services is enabling healthcare professionals to consult, diagnose, advise or perform tasks remotely, enabling them to treat more patients in their own homes or consult on cases on the other side of the world. The security of sensitive user information is critical to effective and efficient delivery of healthcare services. Artificial intelligence (AI) and blockchain technology are identified as key drivers of emerging telehealth systems, enabling efficient delivery of telehealth services to billions of patients globally. Specifically, AI facilitates the processing and analysis of complex telehealth data, and blockchain technology offers decentralised, transparent, traceable, reliable, trustful, and provable security to telehealth systems.

This edited book reviews security and privacy issues in traditional telehealth systems and focuses on the technical considerations, potential opportunities and critical challenges currently inhibiting the adoption of AI and blockchain in telehealth systems. The book presents case studies which highlight critical lessons and considers the prospects and societal benefits of AI and blockchain, while providing suitable recommendations for designing future AI and blockchain-based telehealth systems.

Artificial Intelligence and Blockchain Technology in Modern Telehealth Systems is suited to researchers and computer engineers working in healthcare delivery, telemedicine, cybersecurity, data science, AI/ML and those in related fields.

Machine Learning in Medical Imaging and Computer Vision

Thu, 10 Aug 2023 13:48:33 GMT

Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment.

This edited book discusses feature extraction processes, reviews deep learning methods for medical segmentation tasks, outlines optimisation algorithms and regularisation techniques, illustrates image classification and retrieval systems, and highlights text recognition tools, game theory, and the detection of misinformation for improving healthcare provision.

Machine Learning in Medical Imaging and Computer Vision provides state of the art research on the integration of new and emerging technologies for the medical imaging processing and analysis fields. This book outlines future directions for increasing the efficiency of conventional imaging models to achieve better performance in diagnoses as well as in the characterization of complex pathological conditions.

The book is aimed at a readership of researchers and scientists in both academia and industry in computer science and engineering, machine learning, image processing, and healthcare technologies and those in related fields.

Technologies for Healthcare 4.0

Fri, 07 Jul 2023 12:49:59 GMT

There are a growing number of challenges in handling medical data in order to provide an effective healthcare service in real-time. Bridging the gap between patient expectations and their experiences needs effective collaboration and connectivity across the healthcare ecosystem. The success of joined-up care relies on patient data being shared between all active stakeholders, including hospitals, outreach workers, and GPs. All these needs and challenges pave the way for the next trend of development in healthcare - healthcare 4.0.

This book covers the state-of-the-art approaches in AI, IOT, cloud, big data, deep learning, and blockchain for building intelligent healthcare 4.0 systems, which provide effective healthcare services in real-time.

The editors consider the benefits and challenges of immersive technologies and mixed reality systems for physical and mental health conditions, and outline and discuss the trending technologies supporting the internet of medical things, patient-centred care, assisted medical diagnoses, and electronic medical records.

Technologies for Healthcare 4.0: From AI and IoT to blockchain is essential reading for researchers, scientists, engineers, designers and advanced students in the fields of computer science, computer vision, pattern recognition, machine learning, imaging, feature engineering, IOT, AI, signal processing, blockchain and big data for healthcare and those in adjacent fields.

Medical Imaging Informatics

Fri, 07 Jul 2023 12:49:02 GMT

Medical imaging informatics play an important role in the effectiveness of present-day healthcare systems. Advancement of artificial intelligence, big data analytics, and internet of things technologies contribute greatly to various healthcare applications. Artificial intelligence techniques are contributing to improvements with traditionally human-based systems and ensuring that the accuracy of prediction and diagnosis is being continually enhanced. The development of reliable and accurate healthcare models is becoming ever more possible with the help of machine learning and deep learning technologies. Artificial intelligence has the power to solve many complex problems in medical imaging and is a technology that will help to design the future of many healthcare systems.

This edited book highlights and addresses various issues in medical imaging and provides viable solutions utilising artificial intelligence and big data tools. This book discusses techniques, algorithms, and tools which help build and develop research practices, platforms, and applications in medical image informatics.

Medical image enhancement, big data analytics and artificial intelligence models are discussed with relation to applications in the detection of cancer, autism, allergies and diabetes. The design and development of internet of medical things and virtual reality tools for mental health disorders are also explored.

This book is suitable reading for researchers and scientists, in both academia and industry, working in computer science and engineering, machine learning, image processing, and healthcare technologies. Those in aligned professions, such as healthcare practitioners, administrators, designers and developers may also find the subject matter of interest.