bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hidden Markov Models
Authors: Even Jeremie , Abbas Ali 
Published: April 2006

Abstract

A
hidden Markov model (HMM)
is a
system
that evolves over time with
probabilistic Markov dynamics
, and whose states are
hidden
(unobservable). One of the most studied applications of hidden Markov models is the recognition of patterns in spoken language to identify words. This is the main purpose of speech recognition theory. HMM theory has evolved to include many other fields such as biosciences, computer vision, communications, acoustics, and finance. This chapter presents an introduction to hidden Markov models and is organized as follows. The first section introduces the fundamentals of hidden Markov models. The next section discusses three fundamental problems and their applications: 1. The evaluation problem: given a model and a sequence of observations, how do we compute the probability of the sequence (likelihood of the sequence given the model)? 2. The decoding problem (most probable explanation): given a model and a sequence of observations, what is the optimal, in some meaningful sense, sequence of states that produced the observations? 3. The estimation problem: given a sequence of observations, how do we adjust the model parameters to maximize the probability of getting the sequence?

The third section presents an application of hidden Markov models in computational biology.
Higher‐Order Statistics
Authors: Hadjileontiadis Leontios J. , Panas Stavros M. , Rekanos Ioannis T. 
Published: April 2006

AbstractIn the past 15 years, we have witnessed an expansion of higher‐order statistics (spectra) (HOS)‐based techniques, which have been applied to a wide range of signal processing and system theory problems. This expansion is a direct result of the advantageous properties of HOS, which make them favorable in problems where nonGaussianity, nonminimum phase, colored noise, or nonlinearities are important and must be accounted for. To this end, HOS have been applied to the design of advanced communications, sonar, radar, speech, biomedical, geophysical, and imaging systems. The purpose of the present study is to review the main aspects of the application of HOS in the biomedical engineering field by presenting biomedical application problems that can directly benefit from the use of HOS, in order to aid the reader in grasping the utility of HOS‐based techniques within the biomedical signal processing framework. Fundamental definitions and properties of HOS are initially presented, pointing out the general reasons that are behind the use of HOS in signal processing (i.e., extraction of information because of deviations from normality, estimation of the phase of parametric signals, and detection and characterization of the properties of nonlinear mechanisms that generate time series). Next, a variety of HOS‐based biomedical applications are described, referring to detection of nonlinearities (e.g., quadratic phase‐coupling), denoising‐signal estimation, system identification, classification, and compression issues, and involving analysis of electroencephalogram, electrocardiogram, surface electromyogram, electroneurogram, bioacoustic signals (lung, heart, bowel sounds), tremor, and tissue signatures. Finally, an extensive list of references in the area is provided as a companion aid to the reader for further exploitation of HOS.
Hilbert Transform
Authors: Saad Ziad 
Published: April 2006

Abstract

The Hilbert Transform of real‐valued time signal function
s
(
t
) is another real‐valued function that differs only in phase from
s
(
t
). The transform leaves the amplitude spectrum of
s
(
t
) unchanged, but changes the positive frequency phase spectrum by
π
/2 and the negative frequency phase spectrum by −
π
/2. For example, the Hilbert Transform of a sine function is a cosine function of equal magnitude. In biomedical applications, the Hilbert Transform is mostly used to describe the instantaneous amplitude and frequency of oscillatory signals such as respiration movements and narrow band signals such as filtered electroencephalograms (EEG). In addition, the Hilbert Transform is used to estimate delay differences between signals.

We present the various properties and parameters derived from the Hilbert Transform with emphasis on their applicability to biomedical signal analysis.
Hippocampus
Authors: Soltanian-Zadeh Hamid , Elisevich Kost 
Published: April 2006

AbstractThe hippocampus (HC), a discrete anatomical landmark of the mesial temporal lobe of the brain, is part of the limbic system of the brain and subserves both learning and memory. Several clinical pathological entities are affected by the HC so that its clinical investigation has been a focus of intense work. Its intrinsic connectivity with its feedforward and feedback circuitry underlies a complex organization that, when disturbed, can give rise to debilitating conditions such as epilepsy. The application of electroencephalographic and imaging methodologies to the study of this structure has provided a means of not only lateralizing the site of pathology but determining the nature of the condition.
Independent Component Analysis
Authors: Boscolo Riccardo 
Published: April 2006

AbstractThe term Independent Component Analysis (ICA) broadly refers to the set of statistical principles as well as to the estimation algorithms derived from them, whose aim is to extract statistically independent components in the data, generally through some filtering procedure that can be linear or non‐linear, act on a batch of data at the time or process the data samples as they become available (adaptive).The field of Independent Component Analysis can be seen as stemming from the groundwork of several researchers, who in the last decade of the 20th century investigated fundamental statistical problems, such as the relationship between gaussianity and statistical dependence, redundancy reduction principles, and information preserving networks.
Electromyography (EMG) Modeling
Authors: Dimitrova N.A. , Dimitrov G.V. 
Published: April 2006

Abstract
Modeling is a measure of our understanding of processes and a tool for checking hypotheses and making prognoses. Any attempt to understand and explain electromyographic (EMG) signals, their characteristics, and changes is related to modeling. The authors’ aim is to present the main ideas of EMG modeling in terms understandable to people without special mathematical education. The main physical regularities that rule the formation of electrical potentials in a volume conductor (extracellular medium) are pictorially demonstrated and can be realized ignoring equations. Potentials produced by individual muscle fibers are the basis of EMG signals, which is why a more detailed picture of the physical basis for modeling single‐fiber potentials (SFPs) generated in a homogeneous volume conductor of infinite extent is presented. It is demonstrated why the shape of SFP changes with distance from the active fibers, why amplitude of EMG signal can be an unreliable characteristic, and why detection of EMG by needle and surface electrodes has different sensitivity to prolonged phase of repolarization or to after‐depolarization in intracellular action potential (IAP) and to IAP propagation velocity along the fiber. Description of some equations resulting in fast calculation of the motor unit potentials and interference EMG are presented. The latest papers on mathematical modeling that take into account inhomogeneous and restricted muscle tissue are discussed.
Knowledge Engineering
Authors: Povalej Petra , Mertik Matej , Kokol Peter 
Published: April 2006

Abstract“What is knowledge?” According to Webster's Dictionary, knowledge is defined as a fact or condition of knowing something with familiarity gained through experience or association. It may be described as a set of models that describe various properties and behaviors within a considered domain. Knowledge can be recorded in an individual brain or stored in organizational processes, products, facilities, systems, and documents. Usually the knowledge about particular domain is captured in a knowledge base, which is the assembly of all of the information and knowledge of a specific field of interest. According to the Online Decision Support Systems Glossary, a knowledge base is defined as a collection of facts, rules, and procedures, which are organized within schemas.A field within artificial intelligence that develops knowledge‐based systems (KBS) is called knowledge engineering (KE). Such systems are computer systems that deal with complex problems by making use of knowledge. The development of KBS involves identifying a real‐world problem‐solving task, representing the key components of this task in the KBS, and implementing the inference process that produces solutions. Therefore, three key processes are involved in KE. First, there is a process of capturing the knowledge from the domain experts‐knowledge acquisition that is followed by the task of producing a representation of the problem that captures the key features. At the end, there is a task of developing an inference mechanism that describes the interactions involved in deriving solutions. We will present these processes in more detail in the following paragraphs.
Lung Tissue Viscoelasticity
Authors: Suki Béla , Lutchen Kenneth R. 
Published: April 2006

AbstractThe viscoelastic properties of connective tissues play fundamental roles in the functioning of many organs and organisms as well as in the interactions of these macroscopic biosystems with the complex environment they live in. An emerging view exists that the viscoelastic properties of the extracellular matrix of the lung reflect, to a large extent, the underlying complexity of its structure. This chapter summarizes the phenomenological description of lung tissue viscoelasticity and its relation to tissue composition and structure. In particular, it is shown that the constitutive equation and, hence, the stress relaxation of the lung tissue follows a power law functional form. The constituents of the connective tissue mainly determine the proportionality constants, whereas the complex structural organization of the tissue is responsible for the power law and the numerical value of the exponent. It is argued that the power law constitutive equation develops from the complexity of the structure and the interactions among the components. A concise mathematical description of the constitutive equation of the lung tissue can be based on fractional calculus, which is related to the fractal properties of the collagen‐elastin fiber network embedded in the proteoglycan matrix.
Markov Chains
Authors: Even Jeremie , Abbas Ali E. 
Published: April 2006

Abstract
Markov chains represent the evolution of a system with a first‐order probabilistic dependence. They are often used to describe population processes modeling of the evolution of populations analogous to biological populations. Markov chains are named after the Russian mathematician Andrey Alexeyevitch Markov, who established their first results. In this article, the authors provide a detailed introduction to the theory of Markov chains and present several applications and examples for their results.
Medical Devices, Design and Modification of
Authors: Hyman William A. 
Published: April 2006

AbstractThe design of medical devices is generally a complex and demanding process, often with high potential risk to patients if the device does not function as envisioned. The initial challenge is to correctly capture clinical requirements and to manipulate and to translate these requirements into a technically competent and reliable device that can and will be used effectively in the clinical environment. Successful medical device design includes active consideration of the human factors aspects of user needs and an adequate system safety and risk analysis. Instructions for use including installation and service and manufacturing must also be considered throughout the design cycle. Modification of existing designs whether by the original manufacturer or by the device owner, or a third party, requires particular care so that carefully made decisions during the initial design cycle are not undone by a hastily conceived fix or upgrade. The reuse of devices labeled by the manufacturer as single use only is one example of a “modification” that has received considerable attention and FDA scrutiny. The design process is subject to FDA regulation under the Design Controls section of the Quality Systems Regulations. Compliance with these regulations requires broad knowledge of them by everyone on the design team.
Risk Management for Medical Devices
Authors: Ciarkowski Arthur 
Published: April 2006

Abstract
This article discusses basic concepts of risk management and regulation of medical devices. Risk management is a procedure to reduce the risks involved with a medical device whereas the regulation of medical devices is a public health tool to assure the safety and effectiveness of devices introduced into the market place. Both risk management and regulation are interrelated and take place throughout the entire life cycle of the device, from design through maintenance and obsolescence. As the risks associated with the use of a medical device may vary with the environment where they are used, (e.g., hospital, clinical, home), the risks must be identified and acceptance criteria established to assure that the device perform safely and effectively in its intended environment.
Medical Robotics in Surgery
Authors: Cenk Çavuşoğlu M. 
Published: April 2006

AbstractThis article provides an overview of medical robotic systems and technologies. After introduction of the basic concepts of medical robotics, and introducing a classification of medical robotic systems based on the operator interaction modality, the article discusses the general application areas of robotics technology in medical applications. The presentation is concluded with a discussion on the major research challenges in the area.
Microwave Imaging
Authors: Hinrikus Hiie , Riipulk Jevgeni 
Published: April 2006

AbstractThe overview of applications of microwaves in medical diagnostics is presented. The advantages and disadvantages of passive and active microwave imaging systems, radiometry, tomography, and the confocal radar technique, are analyzed. The limited spatial resolution as well as difficulties in image reconstruction and interpretation of the results do not allow microwave imaging to be successful in competition with technologies for traditional medical imaging. However, microwave imaging provides higher sensitivity with respect to physical or physiological factors of clinical interest, such as temperature, water content, blood‐flow rate, and vascularization/angiogenesis. The data achieved by microwave technology may be possible to relate to the biology of physiological processes, especially in the brain and heart; infections; hyperplasia; and cancer.The potential advantages of active microwave imaging arise mainly from the high contrast of electrical properties between normal and malignant human breast tissues. Active microwave imaging for the detection of breast tumors is an emerging technique to complement existing x‐ray mammography. The nonionizing microwave radiation does not cause changes on an atomic and a molecular level and is much less harmful for biological tissues than ionizing radiation. A short historical overview about microwave radiometry and tomography is done. The theoretical background of radiometric measurements as well as a short description of different methods and instrumentation developed for data visualization and image reconstruction are presented.
Molecular Electronics
Authors: Rinaldi Ross , Maruccio Giuseppe 
Published: April 2006

AbstractMoore's Law, the 1965 prediction by Intel co‐founder Gordon Moore that manufacturers would double the number of transistors on a chip every 18 months, has been fulfilled for four decades by the semiconductor industry. But the latest edition of the annual International Technology Roadmap for Semiconductors—a joint effort of semiconductor industry associations in Europe, Japan, Korea, Taiwan, and the United States—lists reasons for thinking that this may soon change. The Roadmap explores “technology nodes”—advances needed to keep shrinking the so‐called DRAM half‐pitch, half the spacing between cells in memory chips. Currently, the industry is moving to a DRAM half‐pitch of 120 nanometers. The Roadmap forecasts that researchers must lower that figure to 35 nanometers by 2014, simply to continue doubling the number of transistors.In various laboratories worldwide, minimum features sizes that are a factor of 10 smaller (or more) than the 120‐nm node have been demonstrated. However the SIA (Semiconductor Industry Association) Roadmap projects that even though the miniaturization trend will continue for another 15 to 20 years, it is becoming increasingly difficult to continue to down‐scale because of real physical limitations including size of atoms, wavelengths of radiation used for lithography, interconnect scheme. etc.One of the potential roadblocks to continue the scaling beyond the 50‐nm node is the molecular and biomolecular electronics. In this article we report on fundamental concepts and recent advances in the field of molecular electronics.
Electrodiagnosis in Neuromuscular Disorders
Authors: van Alfen Nens , Zwarts Machiel J 
Published: April 2006

AbstractThis article will deal with the electrodiagnostic assessment of the various neuromuscular disorders. For clarity, they will broadly be divided in three major groups: neurogenic disorders affecting anterior horn cells, ganglia, roots, plexuses, or nerves; myopathies; and neuromuscular junction disorders. The mainstay of the diagnostic tools used relies on electroneurography (nerve conduction studies) and electromyography (needle EMG) techniques, commonly referred to as EMG. A short introduction to nerve conduction studies is given in the second section. For an in‐depth discussion of the various needle EMG techniques, the authors refer the reader to the other articles in this section.
Neural Networks
Authors: Cohen Maurice E. 
Published: April 2006

AbstractThe first artificial neural network was designed in the late 1950s, not long after the development of the first commercial computers. The concept behind neural networks is to use structures of the biological nervous system to develop computer‐based decision‐making tools. The basic functional structure of the neuron was used, including multiple inputs that are combined to produce an output if a predetermined threshold value is achieved. In addition, the parallel nature of the biological nervous system was included in the design. Although the early networks were limited in ability and handicapped by the computers of the time in terms of memory and processing speed, subsequent generations of neural networks have been used to develop robust learning systems based on both supervised and unsupervised learning techniques that have been applied to decision making in biomedicine. These applications are broad based, including clinical decision making, bioinformatics, signal analysis, and image processing.
Neural Control of Assistive Technology
Authors: Taylor Dawn 
Published: April 2006

AbstractFor severely paralyzed individuals, controlling various assistive devices is rapidly becoming a reality. Direct neural control requires tapping into a person's intended actions either directly by decoding brain activity or indirectly by tapping into remaining peripheral nerves or muscles to deduce the desired movements of the person. These neural command signals can be used to drive a wide range of assistive technologies including computer‐based communication programs and environmental controls, assistive robots, and even Functional electrical stimulation (FES) systems designed to restore movement to paralyzed limbs. Both field potentials, such as electroencephalograms (EEGs), and multiunit activity (i.e., action potentials detected from many individual neurons via intracortical microelectrodes) have been used for direct brain control of various devices. Field potentials can be measured noninvasively via large scalp electrodes or invasively via implanted electrode (e.g., imbedded in the skull, placed on the brain surface). The firing patterns of individual neurons as well as the power spectra of recorded field potentials naturally have characteristic changes that accompany attempted or imagined movements. These native movement‐related patterns in the brain signals can be decoded in real‐time and used to command an assistive device. However, with practice, subjects can learn to modulate their recorded brain activity in new ways that can potentially convey a desired device command more effectively than by using the naturally occurring signals alone. This ability to retrain the brain to produce more reliable assistive device commands has been successfully demonstrated with both field potentials and multiunit activity.
Neuromuscular Stimulation
Authors: Grill Warren M. 
Published: April 2006

Abstract
This contribution describes electrical activation of the neuromuscular system with electrodes positioned in or on skeletal muscle. The mechanisms of neuromuscular stimulation are described, with reference to transmission at the neuromuscular junction, to illustrate that it is the motor nerve, rather than the muscle, that is stimulated. The two methods to modulate muscle force, recruitment and rate modulation, are reviewed with reference to their basis in the properties of the neuromuscular system including the force‐frequency relationship of skeletal muscle and the strength‐duration, current‐distance, and current‐diameter relationships for excitation of motor nerve fibers. The elements that impact the shape of the input‐output (recruitment) curve of muscle force as a function of stimulation intensity are presented, including a description of position‐dependent recruitment. In addition, the biomechanical impacts on motor output including the length‐tension properties of skeletal muscle and the transformation of muscle force to joint moment are reviewed. This contribution serves as a comprehensive review of the fundamental aspects of neuromuscular stimulation.
Neuromuscular Systems
Authors: Murray-Smith David J. 
Published: April 2006

AbstractThe biological systems involved in the regulation of posture and control of movement are highly complex and have attracted much attention from biomedical engineers and neurophysiologists. As well as offering possibilities for the treatment of neuromuscular diseases, improved understanding of these systems is important for the management of patients who have suffered strokes or spinal injuries. Control theory provides a useful framework for analysis of neuromuscular systems and mathematical modeling and computer simulation techniques used for engineering systems analysis have proved to be of considerable value in this field. Neuromuscular systems are also of considerable interest to those involved in robotics research and biologically‐inspired robots that have features similar to those of the neuromuscular system are becoming increasingly important. Techniques applied in biomimetic robots are starting to influence the currently accepted theories about the hierarchical structure of the neuromuscular system.