Although somatosensory system works in tandem with the motor system in biology, the majority of the prosthetics research and commercial efforts had focused on accommodating movement deficits. With the development of neuroprostheses in the last 15 years, it has become evident that somatosensory input (mainly as touch and proprioception) is essential for motor control, manipulating objects, and embodiment, in addition to its primary role for sensory perception. Somatosensory Feedback for Neuroprosthetics covers all relevant aspects to facilitate learning and doing research and development in the field. To understand the properties of the body to create viable solutions, this book starts with chapters reviewing the basic anatomy, physiology, and psychophysics of the somatosensory system, sensorimotor control, and instrumentation. Some sections are dedicated to invasive (peripheral and central, mainly cortical) and noninvasive (vibrotactile, electrotactile, etc.) approaches. Final chapters cover future technologies such as novel sensors and electrodes, safety, and clinical testing, and help to make up future prospects for this field with an emphasis on development and end use. With contributions from renowned experts, the contents include their recent findings and technical details necessary to understand those findings.

This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental disorders. It includes application of feed-forward neural networks, deep generative models, convolutional neural networks, graph convolutional networks, and recurrent neural networks in the field of diagnosis of neurological disorders. Along with this, data pre-processing including scaling, correction, trimming, normalization is also included. Offers a detailed description of the deep learning approaches used for the diagnosis of neurological disorders Demonstrates concepts of deep learning algorithms using diagrams, data tables, and examples for the diagnosis of neurodegenerative disorders; neurodevelopmental, and psychiatric disorders. Helps build, train, and deploy different types of deep architectures for diagnosis Explores data pre-processing techniques involved in diagnosis Include real-time case studies and examples This book is aimed at graduate students and researchers in biomedical imaging and machine learning.

This new edition presents an authoritative account of the current state of brain biomechanics research for engineers, scientists and medical professionals. Since the first edition in 2011, this topic has unquestionably entered into the mainstream of biomechanical research. The book brings together leading scientists in the diverse fields of anatomy, neuroimaging, image-guided neurosurgery, brain injury, solid and fluid mechanics, mathematical modelling and computer simulation to paint an inclusive picture of the rapidly evolving field. Covering topics from brain anatomy and imaging to sophisticated methods of modeling brain injury and neurosurgery (including the most recent applications of biomechanics to treat epilepsy), to the cutting edge methods in analyzing cerebrospinal fluid and blood flow, this book is the comprehensive reference in the field. Experienced researchers as well as students will find this book useful.

The text covers recent advances in artificial intelligence, smart computing, and their applications in augmenting medical and health care systems. It will serve as an ideal reference text for graduate students and academic researchers in diverse engineering fields including electrical, electronics and communication, computer, and biomedical. The book- Presents architecture, characteristics, and applications of artificial intelligence and smart computing in health care systems Highlight privacy issues faced in health care and health informatics using artificial intelligence and smart computing technologies. Discusses nature-inspired computing algorithms for the brain-computer interface. Covers graph neural network application in the medical domain. Provides insights into the state-of-the-art Artificial Intelligence and Smart Computing enabling and emerging technologies. This book text discusses recent advances and applications of artificial intelligence and smart technologies in the field of healthcare. It highlights privacy issues faced in health care and health informatics using artificial intelligence and smart computing technologies. It covers nature-inspired computing algorithms such as genetic algorithms, particle swarm optimization algorithms, and common scrambling algorithms to study brain-computer interfaces. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and biomedical engineering.

Highlights the contributions of different optimization techniques, decision analytics (predictive, prescriptive, and descriptive), multi-criteria decision making "Helps develop intelligent machines to provide solutions to real-world problems, which are not modelled or are too difficult to model mathematically in hospital management systems " Discusses machine learning-based analytics such as GAN networks, autoencoders, computational imaging, quantum computing will be rigorously applied to smart cloud computing Explores evolutionary algorithms that demonstrate their ability as robust approaches to cope with the fundamental steps of image processing, image analysis, and computer vision pipeline (e.g., restoration, segmentation, registration, classification, reconstruction, or tracking), Creates a bridge between Industrial Engineering concepts and Computational Intelligence for designing complex and convoluted hospital management problems

Most of the real-life signals are non-stationary in nature. The examples of such signals include biomedical signals, communication signals, speech, earthquake signals, vibration signals, etc. Time-frequency analysis plays an important role for extracting the meaningful information from these signals. The book presents time-frequency analysis methods together with their various applications. The basic concepts of signals and different ways of representing signals have been provided. The various time-frequency analysis techniques namely, short-time Fourier transform, wavelet transform, quadratic time-frequency transforms, advanced wavelet transforms, and adaptive time-frequency transforms have been explained. The fundamentals related to these methods are included. The various examples have been included in the book to explain the presented concepts effectively. The recently developed time-frequency analysis techniques such as, Fourier-Bessel series expansion-based methods, synchrosqueezed wavelet transform, tunable-Q wavelet transform, iterative eigenvalue decomposition of Hankel matrix, variational mode decomposition, Fourier decomposition method, etc. have been explained in the book. The numerous applications of time-frequency analysis techniques in various research areas have been demonstrated. This book covers basic concepts of signals, time-frequency analysis, and various conventional and advanced time-frequency analysis methods along with their applications. The set of problems included in the book will be helpful to gain an expertise in time-frequency analysis. The material presented in this book will be useful for students, academicians, and researchers to understand the fundamentals and applications related to time-frequency analysis.

This book describes a comprehensive framework for hardware/software co-design, optimization, and use of robust, low-cost, and cyberphysical digital microfluidic systems. Readers with a background in electronic design automation will find this book to be a valuable reference for leveraging conventional VLSI CAD techniques for emerging technologies, e.g., biochips or bioMEMS. Readers from the circuit/system design community will benefit from methods presented to extend design and testing techniques from microelectronics to mixed-technology microsystems. For readers from the microfluidics domain, this book presents a new design and development strategy for cyberphysical microfluidics-based biochips suitable for large-scale bioassay applications. * Takes a transformative, "cyberphysical" approach towards achieving closed-loop and sensor feedback-driven biochip operation under program control; * Presents a "physically-aware" system reconfiguration technique that uses sensor data at intermediate checkpoints to dynamically reconfigure biochips; * Enables readers to simplify the structure of biochips, while facilitating the "general-purpose" use of digital microfluidic biochips for a wider range of applications.

Microfluidic Devices for Biomedical Applications, Second Edition provides updated coverage on the fundamentals of microfluidics, while also exploring a wide range of medical applications. Chapters review materials and methods, microfluidic actuation mechanisms, recent research on droplet microfluidics, applications in drug discovery and controlled-delivery, including micro needles, consider applications of microfluidic devices in cellular analysis and manipulation, tissue engineering and their role in developing tissue scaffolds, and cover the applications of microfluidic devices in diagnostic sensing, including genetic analysis, low-cost bioassays, viral detection, and radio chemical synthesis. This book is an essential reference for medical device manufacturers, scientists and researchers concerned with microfluidics in the field of biomedical applications and life-science industries.

Globalization and industrialization involve a number of reactions, products, extractions, and separations that require the use of organic solvents. These solvents are responsible for a number of ecological concerns, including atmospheric and land toxicity. Conventional organic solvents are regarded as volatile organic compounds; some are even limited due to their potential for ozone layer depletion. While supercritical liquids exhibit physical properties that could make them ideal substitutes for these volatile compounds, there is particular interest in the use of carbon dioxide as a solvent of crude material. In particular, carbon dioxide has apparent 'green' properties, like its noncombustible nature, the fact that it is generally nonpoisonous, and its relative inertness. Thus, the use of supercritical carbon dioxide can provide practical improvements to the sustainability of industrial products and processes. This book provides in-depth literature in the area of industrial green processes, focusing on the separation, purification, and extraction of compounds utilizing supercritical carbon dioxide as a green solvent.

This book includes high-quality papers presented at the Second International Symposium on Computer Vision and Machine Intelligence in Medical Image Analysis (ISCMM 2021), organized by Computer Applications Department, SMIT in collaboration with Department of Pathology, SMIMS, Sikkim, India, and funded by Indian Council of Medical Research, during 11 - 12 November 2021. It discusses common research problems and challenges in medical image analysis, such as deep learning methods. It also discusses how these theories can be applied to a broad range of application areas, including lung and chest x-ray, breast CAD, microscopy and pathology. The studies included mainly focus on the detection of events from biomedical signals.

Natural compounds obtained from plants represent a tremendous global market due to their use as food additives, cosmetics, in agriculture and in pharmaceuticals. This book provides up-to-date information on various strategies and methods for producing compounds of interest. Leading researchers discuss the latest advances in environmentally friendly natural compound production from plants, making the book a valuable resource for biotechnologists, pharmacists, food technologists and researchers working in the medical and healthcare industries.

Current Developments in Biotechnology and Bioengineering: Current Advances in Solid-State Fermentation provides knowledge and information on solid-state fermentation involving the basics of microbiology, biochemistry, molecular biology, genetics and principles of genetic engineering, metabolic engineering and biochemical engineering. This volume of the series is on Solid-State fermentation (SSF), which would cover the basic and applied aspects of SSF processes, including engineering aspects such as design of bioreactors in SSF. The book offers a pool of knowledge on biochemical and microbiological aspects as well as chemical and biological engineering aspects of SSF to provide an integrated knowledge and version to the readers.

This book highlights the latest advances in bioMEMS for biosensing applications. It comprehensively reviews different detection methods, including colorimetric, fluorescence, luminescence, bioluminescence, chemiluminescence, biochemiluminescence, and electrochemiluminescence, and presents various bioMEMS for each, together with recent examples. The book also offers an overview of the history of BioMEMS and the design and manufacture of the first bioMEMS-based devices.

This book concerns issues related to biomathematics, medicine, or cybernetics as practiced by engineers. Considered population dynamics models are still in the interest of researchers, and even this interest is increasing, especially now in the time of SARS-CoV-2 coronavirus pandemic, when models are intensively studied in order to help predict its behaviour within human population. The structures of population dynamics models and practical methods of finding their solutions are discussed. Finally, the hypothesis of the existence of non-trivial ergodic properties of the model of erythropoietic response dynamics formulated by A. Lasota in the form of delay differential equation with unimodal feedback is analysed. The research can be compared with actual medical data, as well as shows that the structures of population models can reflect the dynamic structures of reality.

Digital Health: Exploring Use and Integration of Wearables is the first book to show how and why engineering theory is used to solve real-world clinical applications, considering the knowledge and lessons gathered during many international projects. This book provides a pragmatic A to Z guide on the design, deployment and use of wearable technologies for laboratory and remote patient assessment, aligning the shared interests of diverse professions to meet with a common goal of translating engineering theory to modern clinical practice. It offers multidisciplinary experiences to guide engineers where no clinically advice and expertise may be available. Entering the domain of wearables in healthcare is notoriously difficult as projects and ideas often fail to deliver due to the lack of clinical understanding, i.e., what do healthcare professionals and patients really need? This book provides engineers and computer scientists with the clinical guidance to ensure their novel work successfully translates to inform real-world clinical diagnosis, treatment and management.

This book reports on the latest advances in concepts and further developments of principal component analysis (PCA), addressing a number of open problems related to dimensional reduction techniques and their extensions in detail. Bringing together research results previously scattered throughout many scientific journals papers worldwide, the book presents them in a methodologically unified form. Offering vital insights into the subject matter in self-contained chapters that balance the theory and concrete applications, and especially focusing on open problems, it is essential reading for all researchers and practitioners with an interest in PCA.

Lignin-based Materials for Biomedical Applications: Preparation, Characterization, and Implementation explores the emerging area of lignin-based materials as a platform for advanced biomedical applications, guiding the reader from source through to implementation. The first part of the book introduces the basics of lignin, including extraction methods, chemical modifications, structure and composition, and properties that make lignin suitable for biomedical applications. In addition, structural characterization techniques are described in detail. The next chapters focus on the preparation of lignin-based materials for biomedical applications, presenting methodologies for lignin-based nanoparticles, hydrogels, aerogels, and nanofibers, and providing in-depth coverage of lignin-based materials with specific properties-including antioxidant properties, UV absorbing capability, antimicrobial properties, and colloidal particles with tailored properties-and applications, such as drug and gene delivery, and tissue engineering. Finally, future perspectives and possible new applications are considered. This is an essential reference for all those with an interest in lignin-based materials and their biomedical applications, including researchers and advanced students across bio-based polymers, polymer science, polymer chemistry, biomaterials, nanotechnology, materials science and engineering, drug delivery, and biomedical engineering, as well as industrial R&D and scientists involved with bio-based polymers, specifically for biomedical applications.

Biomedical Signal Analysis for Connected Healthcare provides rigorous coverage on several generations of techniques, including time domain approaches for event detection, spectral analysis for interpretation of clinical events of interest, time-varying signal processing for understanding dynamical aspects of complex biomedical systems, the application of machine learning principles in enhanced clinical decision-making, the application of sparse techniques and compressive sensing in providing low-power applications that are essential for wearable designs, the emerging paradigms of the Internet of Things, and connected healthcare.

This book introduces the new concept of "nanozyme", which refers to nanomaterials with intrinsic enzymatic activity, rather than nanomaterials with biological enzymes incorporated on the surface. The book presents the cutting-edge advances in nanozyme, with emphasis on state-of-the-art applications in many important fields, such as in the biomedical fields and for environmental protection. The nanozyme is a totally new type of artificial enzyme and exhibits huge advantages over natural enzymes, including greater stability, low cost, versatility, simplicity, and suitability for industry. It is of interest to university researchers, R&D engineers, as well as graduate students in nanoscience and technology, and biology wishing to learn the core principles, methods, and the corresponding applications of "nanozyme".

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians.

Biomedical Engineering Tools for Management of Patients with COVID-19 presents biomedical engineering tools under research (and in development) that can be used for the management of COVID-19 patients, along with BME tools in the global environment that curtail and prevent the spread of the virus. BME tools covered in the book include new disinfectants and sterilization equipment, testing devices for rapid and accurate COVID-19 diagnosis, Internet of Things applications in COVID-19 hospitals, analytics, Data Science and statistical modeling applied to COVID-19 tracking, Smart City instruments and applications, and more. Later sections discuss smart tools in telemedicine and e-health. Biomedical engineering tools can provide engineers, computer scientists, clinicians and other policymakers with solutions for managing patient treatment, applying data analysis techniques, and applying tools to help the general population curtail spread of the virus.

Information Physics: Physics-Information and Quantum Analogies for Complex Modeling presents a new theory of complex systems that uses analogy across various aspects of physics, including electronics, magnetic circuits and quantum mechanics. The book explains the quantum approach to system theory that can be understood as an extension of classical system models. The main idea is that in many complex systems there are incomplete pieces of overlapping information that must be strung together to find the most consistent model. This incomplete information can be understood as a set of non-exclusive observer results. Because they are non-exclusive, each observer registers different pictures of reality.

Rapid prototyping is used to design and develop medical devices and instrumentation. This book details research in rapid prototyping of bio-materials for medical applications. It provides a wide variety of examples of medical applications using rapid prototyping, including tissue engineering, dental applications, and bone replacement. Coverage also discusses the emergence of computer aided design in the development of prosthetic devices.

The book set develops a bridge between physiologic mechanisms and diagnostic human engineering. While the first volume is focused on the interface between physiologic mechanisms and the resultant biosignals, this second volume is devoted to the interface between biosignals and biomedical sensors. That is, in the first volume, the physiologic mechanisms determining biosignals are described from the basic cellular level up to their advanced mutual coordination level. This second volume, considers the genesis of acoustic and optic biosignals and the associated sensing technology from a strategic point of view. As a novelty, this book discusses heterogeneous biosignals within a common frame. This frame comprises both the biosignal formation path from the biosignal source at the physiological level to biosignal propagation in the body, and the biosignal sensing path from the biosignal transmission in the sensor applied on the body up to its conversion to a, usually electric, signal. Some biosignals arise in the course of the body's vital functions while others map these functions that convey physiological data to an observer. It is highly instructive how sound and light beams interact with biological tissues, yielding acoustic and optic biosignals, respectively. Discussed phenomena teach a lot about the physics of sound and physics of light (as engineering sciences), and, on the other hand, biology and physiology (as live sciences). The highly interdisciplinary nature of biosignals and biomedical sensors is obviously a challenge. However, it is a rewarding challenge after it has been coped with in a strategic way, as offered here. The book is intended to have the presence to answer intriguing "Aha!" questions.

This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXII International Conference on Neuroinformatics, held on October 12-16, 2020, Moscow, Russia.