bluuebunny/crossref_metadata_2025_split huggingface dataset:
Elasticity
Authors: Fotiadis Dimitrios I. , Protopappas Vasilios C. , Massalas Christos V. 
Published: April 2006

Abstract
The theory of elasticity deals with the systematic study of the stress, strain, and displacement fields in an elastic body under the influence of external forces. In this chapter, we deal with elastic materials (i.e., materials in which the deformation and stress disappear with the removal of the external forces). However, equations for other types of materials are also given. Index notation and Cartesian tensors are presented to enable equations of a continuum to be displayed with remarkable brevity. The concept of stresses and the deformation of a medium are explained. The constitutive or material laws, characterizing the mechanical behavior of the medium, are given. The application of the basic principles of mechanics to the deformable body is described. The elastic properties of biological materials along with examples of selected materials are presented. Also, biomechanical methods for measuring the elastic constants of biological materials as well as applications to diagnosis and treatment are demonstrated. Finally, the problem of free vibrations of an elastic cavity, simulating the human skull, and its solution is given.
Electric Shock
Authors: Morse Michael , Morse Jennifer 
Published: April 2006

AbstractWhen human beings were first designed, the designer must have considered that someday they would live in a society in which they were in constant and close proximity to electrical energy. Human skin is a naturally protective barrier to the flow of electricity, but still, under the right conditions, humans can become part of the electric technology and electrical processes that touch every aspect of our daily lives. Commercially generated electricity has been a part of our world for over 100 years. Injury from electrical contact can run the gamut from momentary pain to extensive physical damage that can impair multiple‐organ systems. Primary mechanisms of electrical injury include thermal injury from the electrical energy imparted during the shock, ventricular fibrillation caused when pacing is interrupted by electrical current flow, and electrically induced respiratory arrest. Secondary injury includes soft tissue damage and broken bones resultant from falls and impacts caused by electrically induced muscle contractions. Other secondary injuries caused by electric arcs include flash burns and blunt force trauma. Although electric shock has been studied for well over a century, unexplained responses to electrical contacts still remain, which lead researchers to conclude that, given the complexity and variability of the human machine, human response to electrical energy is far more complex than was originally believed.
Electrochemical Biosensors
Authors: Catherino Henry A. 
Published: April 2006

AbstractThe technology supporting the development of electrochemical biosensors is a high‐interest area for current research programs. Broad interdisciplinary skills are required for the design and construction of these devices. To provide a brief overview of these biosensors, it is helpful to (1) define what is meant by a biosensor and (2) explain the attributes and properties of an effective sensor. The detection approach is electrochemical in origin. This technical discipline constitutes an area of specialization in its own right. The applicable biomaterials and how they produce detection signals brings together biochemical sciences with electrochemical phenomena. Finally, a discussion of the immobilization technologies is presented, which yields a useful biosensor that is a significant materials science development effort. Also, a list of references is presented that can lead the reader to current texts and major review articles addressing the technology in greater detail.
Electroencephalography (EEG)
Authors: Blinowska Katarzyna , Durka Piotr 
Published: April 2006

AbstractElectroencephalography is a domain concerning recording and interpretation of the electroencephalogram. Electroencephalogram (EEG) is a record of the electric signal generated by the cooperative action of brain cells, or more precisely, the time course of an extracellular field potentials generated by their synchronous action. Electroencephalogram derives from the Greek words enkephalo (brain) and graphein (to write). EEG can be measured by means of electrodes placed on the scalp or directly on the cortex. In the latter case, it is sometimes called electrocorticogram (ECoG). Electric fields measured intracortically were named Local Fields Potentials (LFP). EEG recorded in the absence of an external stimulus is called spontaneous EEG; EEG generated as a response to external or internal stimulus is called an event‐related potential (ERP. The amplitude of EEG of a normal subject in the awake state recorded with the scalp electrodes is 10–100 μV. In case of epilepsy, the EEG amplitudes may increase by almost an order of magnitude. In the cortex, amplitudes are in the range 500–1500 μV.
Electromagnetic Interference and Compatibility
Authors: Silva Ferran , Fernandez Mireya , Riu Pere J. 
Published: April 2006

Abstract
Electromagnetic compatibility (EMC) is defined as the ability of electronic equipment to work as intended in the presence of certain electromagnetic interferences. To achieve this goal in medical equipment, emissions and immunity must be addressed. Emissions from electronic equipment must be limited in order to not create an electromagnetic environment too harsh so that other equipment is unable to work. At the same time, equipment immunity must be achieved to avoid any critical malfunction when the equipment is working in a typical environment where a certain level of perturbation exists.
Extracellular Matrix
Authors: Melrose James , Whitelock John M. 
Published: April 2006

AbstractThe extracellular matrix (ECM) is a complex composite biomaterial with critical structural and functional roles to play in connective tissues. The cells embedded within the ECM provide the biosynthetic machinery for the synthesis and secretion of the complex array of interactive molecules that are required for its assembly. The major components of ECMs are glycoproteins, collagens, proteoglycans, and elastin. ECMs are heterogeneous both between connective tissues and within a single tissue type during development/maturation or with ECM remodeling events associated with pathologic processes. Some connective tissues are highly cellular and contain relatively little ECM (e.g., muscle, kidney, liver) whereas others contain an abundant ECM and relatively few cells (e.g., cartilage, tendon). Some tissues contain mineralized matrices (e.g., bone, dentine) whereas the ECM of others have gel‐like consistencies (e.g., vitreous humour, synovial fluid, Wharton's jelly), reflecting their relative contributions to weight bearing, internal organ cushioning, or lubrication of joint surfaces. For the purposes of this chapter, cartilage was selected as a representative tissue because of its relatively simple structure, containing ∼5% cells but approximately 90% ECM. The chondrocytes in hyaline cartilage, however, have well‐defined pericellular, territorial, and inter‐territorial matrices and characteristic cellular arrangements (chondrons) in the superficial, intermediate, and deep zones of this tissue. It is therefore possible to identify different functional compartments in the cartilage ECM and to categorize the proteins within them, which offers heuristic advantages.
Extracorporeal Membrane Oxygenation
Authors: Conrad Steven A. , Keith Scott L. 
Published: April 2006

AbstractExtracorporeal membrane oxygenation (ECMO) is the provision of long‐term support for severe, potentially reversible cardiopulmonary failure through the use of extrathoracic cannulation and an extracorporeal circuit. The extracorporeal circuit is a modification of the heart‐lung machine used in cardiac bypass surgery and consists of a drainage reservoir, blood pump, artificial lung for oxygen and carbon dioxide exchange, a heat exchanger for maintenance of body temperature, cannulae for vascular access, and connecting tubing. ECMO is used in the management of severe, acute, potentially reversible cardiopulmonary failure in neonates, children, and adults. The modes of support include venoarterial (VA), venovenous (VV), arteriovenous (AV), and hybrid VA and VV (VVA). Venoarterial support bypasses the heart and lungs and provides cardiac and pulmonary support. Venovenous provides gas exchange in the central venous circulation, and it provides pulmonary support only. Arteriovenous is performed using an artificial lung without a pump, but provides carbon dioxide removal only. VVA mode combines partial cardiac support in addition to pulmonary support.The performance of the ECMO circuit depends on several factors. Blood flow is dependent on geometry of the vascular cannulae, tubing, and artificial lung, and it includes laminar and turbulent flow. Pressure‐flow relationships for ECMO circuit components have been published. Gas exchange efficiency in venovenous ECMO is affected by recirculation. The determinants of artificial lung gas transfer are complex but based on principles of passive diffusion. Modern cross‐flow hollow fiber devices that have blood flow exterior to the fibers demonstrate the best blood flow and gas transfer performance.
Fatigue
Authors: Roca-Dorda Joaquin , Roca-González Joaquín , Martínez-Gonzalez Francisco , Guillamón-Frutos Antonio 
Published: April 2006

AbstractThe study of fatigue has increased by leaps and bounds; however, still no universal definition exists and fatigue remains a vague concept used as an umbrella term that comprises of physiological, emotional, and behavioral factors that can result in chronic physical or mental states affecting the cognitive, memory, motoric, and perceptive capacities. Biomedical engineering has faced fatigue evaluation from two different approaches: exercise‐induced fatigue (or metabolic fatigue) and central fatigue (related to the evolution of the subject condition caused by the execution of normal tasks). As metabolic fatigue is a complex mechanism usually discussed by exercise physiology, this work will center on central fatigue detection and evaluation, because of its importance within the field of human factors engineering in occupational health studies. Central fatigue has been referred to in various terms that are more or less mutually interchangeable in the research literature: loss of alertness or vigilance, fatigue, and drowsiness, although no significant difference exists among them.Research has focused on the development of objective and reliable measurement techniques able to quantify the state of human deteriorated condition, although none of the proposed methods has been yet considered to be a gold standard. This work reviews several fatigue indicators that have been reported to reflect the different dimensions resulting from this state [heart‐related measures such as the heart rate (HR) and heart rate variability (HRV), muscles activity through electromyogram (EMG) and mechanomyogram (MMG), spectral analysis of the electroencephalograph (EEG) and event‐related potential (ERP), ocular metrics, speech parameters, etc].
Fiber Optic Sensors
Authors: Yin Stuart (Shizhuo) , Ruffin Paul 
Published: April 2006

Abstract
Fiber optic sensors have become indispensable tools for biomedical study because of their unique features such as (1) high sensitivity, (2) small footprint and endoscopic compatibility, (3) multiple agent distributive sensing capability, and (4) immunity from electromagnetic interference. In this chapter, we will provide a brief overview on the basic principle of fiber optic sensors, types of fiber optic sensors, and their applications to biomedical sensing. As a result of the page limitation, important work in this area may not be included in this chapter. Readers may find those contents in the listed reference material.
Force Measurement
Authors: Anderson I.A. , Lim K. 
Published: April 2006

Abstract
A variety of transduction technologies are available for the measurement of force. Some of the more common ones, including strain gauge and piezoelectric types, are described in this article. There are several important considerations when dealing with force measurement, which include measurement range, linearity, accuracy, sensitivity, frequency response, and aliasing. The accuracy of the force measurement can be limited by incorrect interpretation of the output from a transducer. Errors can occur due to poor sensitivity and nonlinear behavior. The measurement of dynamic force also requires consideration of the frequency response characteristics of a transducer. To avoid large errors, it is important to choose a force transducer with a natural frequency that is significantly greater than the maximum frequency of interest in the force measurement. Choice of transduction technology will deal with most of these issues to a greater or lesser degree. Examples of specific applications of force measurement in biomedical engineering will be presented in this article.
Forward Problem
Authors: de Munck J.C. , Heethaar R.M. 
Published: April 2006

Abstract
Forward problems show up when information is extracted from recorded data, which cannot be assessed directly. An example is the epi‐cardial potential distribution in a healthy volunteer. This article presents a short and qualitative overview of the forward problem, as it appears in biomedical engineering. The main focus is in electroencephalography (EEG) and impedance tomography (EIT). As in real applications the forward problem has to be solved for many combinations of parameters, computational speed is important. Also, the solution needs to be accurate, to avoid systematic errors.
Fuzzy Neural Networks
Authors: Melek William W. , Yeow John T.W. 
Published: April 2006

AbstractThis chapter provides a comprehensive review of fuzzy neural network models. Existing methods to combine fuzzy logic and neural networks are presented. Several classifications of fuzzy neural networks are provided. Those classifications are based on the type of application of the fuzzy neural network. Fuzzy neural network models with supervised learning features are also reviewed in detail. A review of various biomedical engineering applications that use fuzzy neural networks is also presented. Some future medical science applications of fuzzy neural networks are described. A complete bibliography is listed at the end of the chapter.
Haptic Devices and Interfaces
Authors: Repperger Daniel W. , Phillips Chandler A. 
Published: April 2006

AbstractThe sense of touch (somatic or haptic) involving force‐reflecting devices acting on, or in conjunction with, human movement, provides a powerful paradigm to assist individuals in shaping their biodynamic actions in specific tasks or scenarios. Haptics can behave as both a sensor (force display) or an actuator (controller) with much research accomplished in both areas. First a survey on current biomedical and rehabilitation applications is provided. A specific biomedical example is then presented that deals with modulating control motions made by physically challenged individuals that enabled more voluntary control. Finally, the future applicability of haptics or force reflecting technology is given.
Heart Rate Variability (HRV)
Authors: Akselrod Solange , Pinhas Itzik , Davrath Linda R. , Shinar Zvika , Toledo Eran 
Published: April 2006

Abstract
One of the important and challenging fields in cardiovascular research is the investigation of its control mechanisms, which regulate cardiovascular function in response to alterations in physiological, environmental, and emotional states. The main variables under control are heart rate (HR) and blood pressure (BP). Their role is to maintain homeostasis. Essential quantitative information about neural and humoral cardiovascular control can be noninvasively obtained by applying computerized, mathematical, and physical tools for the processing of basic signals, such as heart rate, blood pressure, blood flow, and other related physiological signals.
The measurement of the mean HR (or BP) is of utmost importance and is a first‐order indicator of the patient's well‐being. However, heart rate variability (HRV) completes the picture, expressing by its fluctuations the levels of autonomic neural input to the SA node. It is important to emphasize the noninvasiveness of this approach and its ability to extract valid continuous (beat‐to‐beat) information in the time domain, the frequency domain, and the time‐frequency domain.
Careful HRV analysis reveals noninvasive quantitative information of the parasympathetic, sympathetic (alpha and beta) activity and control (of the SA node), combined with all the active feedback mechanisms. Indirectly, it thus provides information on the BP control, mainly the direct vasomotor neural control as expressed by the reflex HR modulation. Respiration is a crucial contribution to HRV.
HRV provides insight into the autonomic nervous system and allows the computation of sensitive diagnostic parameters to be used for clinical applications.
Higher‐Order Spectral Analysis
Authors: Lipping Tarmo 
Published: April 2006

AbstractThe definitions of higher order moments, cumulants, and spectra are given. Direct and indirect methods of bispectrum estimation are described. The main properties of cumulants are presented together with the symmetry properties of the bispectrum. Finally, some application areas of higher order spectral analysis like removal of Gaussian noise, identification of non‐minimum phase systems, and detecting nonlinearities are discussed. The reader is referred to the literature containing thorough treatment of the subject.
Mechanical Testing
Authors: Gillies Ronald Mark 
Published: April 2006

AbstractMechanical testing is the investigation of the relationship between the structure and properties of a material. It is an avenue to evaluate the design of a structure‐property correlation, where the design must conform to an agreed standard. In biomedical engineering, these mechanical tests are usually the investigation of biocompatible devices and biological tissues. The mechanical tests or simulations may involve joint or organ kinematics and load deformation characteristics of soft tissues. The biomechanics involved in the motion of the joints are complex and require an ability to simulate the contraction and antagonistic relaxation of the involved muscle groups. The majority of mechanical testing specifications are normally defined by standards organizations or published guidelines.
Mechanomyography
Authors: Orizio Claudio , Gobbo Massimiliano 
Published: April 2006

AbstractThis chapter on surface mechanomyography (MMG) is aimed to provide basic information about a relatively new biological signal detectable at the muscle surface during contraction. The text will deal with the definition and terminology; the relationship between the activity of the single motor units and the signal generation process and, as a consequence, on the MMG reliability as a tool to reveal the motor units activation strategy, the relationship between MMG and muscle mechanics to disclose the possible use of this signal to get information about muscle contractile properties; and finally some examples of practical applications in physiological and clinical studies. The goal of this chapter is not to provide an extensive review on the MMG but only to underline some physiological and technical aspects that the spreading of its use may be based on.
Medical Device Acquisition
Authors: Silva Ricardo , Alkhateeb Abdalhameed , Gaumond Roger 
Published: April 2006

Abstract
A clinical institution is always in search of new technology and of new equipment. These purchases can wreck operating budgets if mishandled. The need for each purchase must be well understood, and the total costs, including all installation and operating costs, must be considered. A key element is the negotiation process between the hospital's acquisition committee and the vendor.
Medical Device Industry
Authors: Panescu Dorin 
Published: April 2006

AbstractIn its basic form, medical technology development has been reported since the early days of recorded history. However, most technologies and devices that markedly improved human health and increased life expectancy have been developed in the last half century. Although in the last two decades economic cycles and uncertainties have affected several industries, the medical device sector reaped the benefits of earlier investments and delivered unmatched improvements in life quality. The U.S. medical technology industry is the largest producer of medical devices and diagnostics, with production evaluated at $77 billion in 2002. Medical device firms invested about 11% of their revenues in research and development, more than any other industrial sector with the exception of pharmaceuticals manufacturers. As a result, between 1980 and 2000, the heart attack mortality decreased by about 40%, the stroke deaths declined by 37%, diabetes complications were 25% fewer, and breast cancer mortality was 20% lower. Americans now have a life expectancy at birth of 76.5 years. This article reviews the progress made in the medical device industry in the last 25 years.
Optical Microscopy
Authors: Diaspro Alberto , Usai Cesare 
Published: April 2006

AbstractMicroscopy studies the enlargement of the image of objects too small to be properly seen by the unaided eye. Microscopy accomplishes its task making use of the radiations emitted, absorbed, transmitted, or reflected by the specimen to be observed. The nature of the radiation specifies the type of microscopy: electron microscopy, x‐ray microscopy, acoustic microscopy, etc. The visible part of electromagnetic spectrum is the type of radiation used by optical microscopy.
Pattern Classification
Authors: Hudson Donna L. 
Published: April 2006

Abstract
The first computerized medical decision support systems were based on pattern classification techniques. Early applications were focused on the diagnosis of disease. The process involved determination of parameters that were potentially useful in classification of cases into two or more categories. The parameters, denoted features, were organized as vector components. Numerous learning algorithms were developed that were trained on available data both for supervised learning in which correct classification of cases was known and unsupervised learning in which the classification was unknown and occasionally even the number of classes was unknown. The models were very successful and multiplied rapidly. Applications of pattern recognition in decision making came to be known as pattern classification, whereas pattern recognition is more often associated with recognition of patterns in images. The basic techniques are the same, with the differences originating in the feature identification phase. Neural networks are special types of pattern classification algorithms that use architecture based on biological nervous systems.