Biomedical Engineering is an exciting and emerging interdisciplinary field that combines engineering with life sciences. The relevance of this area can be perceived in our everyday lives every time we go to hospital, receive medical treatment or even when we buy health products such as an automatic blood pressure monitor device. Over the past years we have experienced a great technological development in health care and this is due to the joint work of engineers, mathematicians, physicians, computer scientists and many other professionals. This book introduces a collection of papers organized into three sections that provide state of the art examples of practical applications in Biomedical Engineering in the area of Biomedical Signal Processing and Modelling, Biomaterials and Prosthetic Devices, and Biomedical Image Processing.
System theory is becoming increasingly important to medical applications. Yet, biomedical and digital signal processing researchers rarely have expertise in practical medical applications, and medical instrumentation designers usually are unfamiliar with system theory. System Theory and Practical Applications for Biomedical Signals bridges those gaps in a practical manner, showing how various aspects of system theory are put into practice by industry. The chapters are intentionally organized in groups of two chapters, with the first chapter describing a system theory technology, and the second chapter describing an industrial application of this technology. Each theory chapter contains a general overview of a system theory technology, which is intended as background material for the application chapter. Each application chapter contains a history of a highlighted medical instrument, summary of appropriate physiology, discussion of the problem of interest and previous empirical solutions, and review of a solution that utilizes the theory in the previous chapter. Biomedical and DSP academic researchers pursuing grants and industry funding will find its real-world approach extremely valuable. Its in-depth discussion of the theoretical issues will clarify for medical instrumentation managers how system theory can compensate for less-than-ideal sensors. With application MATLAB® exercises and suggestions for system theory course work included, the text also fills the need for detailed information for students or practicing engineers interested in instrument design. An Instructor Support FTP site is available from the Wiley editorial department: ftp://ftp.ieee.org/uploads/press/baura
Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high-level features from large amounts of raw data. Deep Learning methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of Deep Learning and its applications in the field of Biomedical Engineering. Deep learning has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. Deep Learning provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as Deep Learning and Convolutional Neural Networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use Deep Learning include: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of Deep Learning applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer's, ADHD, and ASD, tumor prediction, as well as translational multimodal imaging analysis. - Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT - Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis - Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks - Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer's, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography
Current demand in biomedical sciences emphasizes the understanding of basic mechanisms and problem solving rather than rigid empiricism and factual recall. Knowledge of the basic laws of mass and momentum transport as well as model development and validation, biomedical signal processing, biomechanics, and capstone design have indispensable roles i
Control Applications for Biomedical Engineering Systems presents different control engineering and modeling applications in the biomedical field. It is intended for senior undergraduate or graduate students in both control engineering and biomedical engineering programs. For control engineering students, it presents the application of various techniques already learned in theoretical lectures in the biomedical arena. For biomedical engineering students, it presents solutions to various problems in the field using methods commonly used by control engineers. - Points out theoretical and practical issues to biomedical control systems - Brings together solutions developed under different settings with specific attention to the validation of these tools in biomedical settings using real-life datasets and experiments - Presents significant case studies on devices and applications
Applications of Multi-Criteria Decision-Making Theories in Healthcare and Biomedical Engineering contains several practical applications on how decision-making theory could be used in solving problems relating to the selection of best alternatives. The book focuses on assisting decision-makers (government, organizations, companies, general public, etc.) in making the best and most appropriate decision when confronted with multiple alternatives. The purpose of the analytical MCDM techniques is to support decision makers under uncertainty and conflicting criteria while making logical decisions. The knowledge of the alternatives of the real-life problems, properties of their parameters, and the priority given to the parameters have a great effect on consequences in decision-making. In this book, the application of MCDM has been provided for the real-life problems in health and biomedical engineering issues. - Provides a comprehensive analysis and application multi-criteria decision-making methods - Presents detail information about MCDM and their usage - Covers state-of-the-art MCDM methods and offers applications of MCDM for health and biomedical engineering purposes
Modern Practical Healthcare Issues in Biomedical Instrumentation describes the designs, applications and principles of several medical devices used in hospitals and at home. The book presents practical devices that can potentially be used for healthcare purposes. Sections cover the use of biosensors to monitor the physiological properties of the human body, focusing on devices used to evaluate, measure and manipulate the biological system, and highlighting practical devices that can potentially be used for healthcare purposes. It is an excellent resource for undergraduate, graduate and post-graduate students of biomedical engineering. - Focuses on devices used to evaluate, measure and manipulate the biological system - Describes the designs, applications and principles of several medical devices used in hospitals and at home - Discusses various application and how their usage will help to aid health care delivery
Practical Biomedical Signal Analysis Using MATLAB® presents a coherent treatment of various signal processing methods and applications. The book not only covers the current techniques of biomedical signal processing, but it also offers guidance on which methods are appropriate for a given task and different types of data. The first several chapters of the text describe signal analysis techniques—including the newest and most advanced methods—in an easy and accessible way. MATLAB routines are listed when available and freely available software is discussed where appropriate. The final chapter explores the application of the methods to a broad range of biomedical signals, highlighting problems encountered in practice. A unified overview of the field, this book explains how to properly use signal processing techniques for biomedical applications and avoid misinterpretations and pitfalls. It helps readers to choose the appropriate method as well as design their own methods.
Materials for Biomedical Engineering: Thermoset and Thermoplastic Polymers presents the newest and most interesting approaches to intelligent polymer engineering in both current and future progress in biomedical sciences. Particular emphasis is placed on the properties needed for each selected polymer and how to increase their biomedical potential in varying applications, such as drug delivery and tissue engineering. These materials are intended for use in diagnoses, therapy and prophylaxis, but are also relatable to other biomedical related applications, such as sensors. Recent developments and future perspectives regarding their use in biomedicine are discussed in detail, making this book an ideal source on the topic. - Highlights the most well-known applications of thermoset and thermoplastic polymers in biological and biomedical engineering - Presents novel opportunities and ideas for developing or improving technologies in materials for companies, those in biomedical industries, and others - Features at least 50% of references from the last 2-3 years
This volume introduces readers to the basic concepts and recent advances in the field of biomedical devices. The text gives a detailed account of novel developments in drug delivery, protein electrophoresis, estrogen mimicking methods and medical devices. It also provides the necessary theoretical background as well as describing a wide range of practical applications. The level and style make this book accessible not only to scientific and medical researchers but also to graduate students.
