Applied Mathematics for the Analysis of Biomedical Data

Models, Methods, and MATLAB

Author: Peter J. Costa

Publisher: John Wiley & Sons

ISBN: 1119269490

Category: Mathematics

Page: 448

View: 6636

Features a practical approach to the analysis of biomedical data via mathematical methods and provides a MATLAB® toolbox for the collection, visualization, and evaluation of experimental and real-life data Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® presents a practical approach to the task that biological scientists face when analyzing data. The primary focus is on the application of mathematical models and scientific computing methods to provide insight into the behavior of biological systems. The author draws upon his experience in academia, industry, and government–sponsored research as well as his expertise in MATLAB to produce a suite of computer programs with applications in epidemiology, machine learning, and biostatistics. These models are derived from real–world data and concerns. Among the topics included are the spread of infectious disease (HIV/AIDS) through a population, statistical pattern recognition methods to determine the presence of disease in a diagnostic sample, and the fundamentals of hypothesis testing. In addition, the author uses his professional experiences to present unique case studies whose analyses provide detailed insights into biological systems and the problems inherent in their examination. The book contains a well-developed and tested set of MATLAB functions that act as a general toolbox for practitioners of quantitative biology and biostatistics. This combination of MATLAB functions and practical tips amplifies the book’s technical merit and value to industry professionals. Through numerous examples and sample code blocks, the book provides readers with illustrations of MATLAB programming. Moreover, the associated toolbox permits readers to engage in the process of data analysis without needing to delve deeply into the mathematical theory. This gives an accessible view of the material for readers with varied backgrounds. As a result, the book provides a streamlined framework for the development of mathematical models, algorithms, and the corresponding computer code. In addition, the book features: Real–world computational procedures that can be readily applied to similar problems without the need for keen mathematical acumen Clear delineation of topics to accelerate access to data analysis Access to a book companion website containing the MATLAB toolbox created for this book, as well as a Solutions Manual with solutions to selected exercises Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® is an excellent textbook for students in mathematics, biostatistics, the life and social sciences, and quantitative, computational, and mathematical biology. This book is also an ideal reference for industrial scientists, biostatisticians, product development scientists, and practitioners who use mathematical models of biological systems in biomedical research, medical device development, and pharmaceutical submissions.

Biomedical Mass Transport and Chemical Reaction

Physicochemical Principles and Mathematical Modeling

Author: James S. Ultman,Harihara Baskaran,Gerald M. Saidel

Publisher: John Wiley & Sons

ISBN: 1119184630

Category: Technology & Engineering

Page: 656

View: 5791

Teaches the fundamentals of mass transport with a unique approach emphasizing engineering principles in a biomedical environment Includes a basic review of physiology, chemical thermodynamics, chemical kinetics, mass transport, fluid mechanics and relevant mathematical methods Teaches engineering principles and mathematical modelling useful in the broad range of problems that students will encounter in their academic programs as well as later on in their careers Illustrates principles with examples taken from physiology and medicine or with design problems involving biomedical devices Stresses the simplification of problem formulations based on key geometric and functional features that permit practical analyses of biomedical applications Offers a web site of homework problems associated with each chapter and solutions available to instructors Homework problems related to each chapter are available from a supplementary website (

Statistical Methods for Modeling Human Dynamics

An Interdisciplinary Dialogue

Author: Sy-Miin Chow,Emilio Ferrer,Fushing Hsieh

Publisher: Routledge

ISBN: 1135262586

Category: Psychology

Page: 448

View: 6891

This interdisciplinary volume features contributions from researchers in the fields of psychology, neuroscience, statistics, computer science, and physics. State-of-the-art techniques and applications used to analyze data obtained from studies in cognition, emotion, and electrophysiology are reviewed along with techniques for modeling in real time and for examining lifespan cognitive changes, for conceptualizing change using item response, nonparametric and hierarchical models, and control theory-inspired techniques for deriving diagnoses in medical and psychotherapeutic settings. The syntax for running the analyses presented in the book is provided on the Psychology Press site. Most of the programs are written in R while others are for Matlab, SAS, Win-BUGS, and DyFA. Readers will appreciate a review of the latest methodological techniques developed in the last few years. Highlights include an examination of: Statistical and mathematical modeling techniques for the analysis of brain imaging such as EEGs, fMRIs, and other neuroscience data Dynamic modeling techniques for intensive repeated measurement data Panel modeling techniques for fewer time points data State-space modeling techniques for psychological data Techniques used to analyze reaction time data. Each chapter features an introductory overview of the techniques needed to understand the chapter, a summary, and numerous examples. Each self-contained chapter can be read on its own and in any order. Divided into three major sections, the book examines techniques for examining within-person derivations in change patterns, intra-individual change, and inter-individual differences in change and interpersonal dynamics. Intended for advanced students and researchers, this book will appeal to those interested in applying state-of-the-art dynamic modeling techniques to the the study of neurological, developmental, cognitive, and social/personality psychology, as well as neuroscience, computer science, and engineering.

Biosignal and Medical Image Processing

Author: John L. Semmlow

Publisher: CRC Press

ISBN: 1439878064

Category: Medical

Page: 448

View: 815

Relying heavily on MATLAB® problems and examples, as well as simulated data, this text/reference surveys a vast array of signal and image processing tools for biomedical applications, providing a working knowledge of the technologies addressed while showcasing valuable implementation procedures, common pitfalls, and essential application concepts. The first and only textbook to supply a hands-on tutorial in biomedical signal and image processing, it offers a unique and proven approach to signal processing instruction, unlike any other competing source on the topic. The text is accompanied by a CD with support data files and software including all MATLAB examples and figures found in the text.

Signal Processing for Neuroscientists

An Introduction to the Analysis of Physiological Signals

Author: Wim van Drongelen

Publisher: Elsevier

ISBN: 9780080467757

Category: Science

Page: 320

View: 5177

Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the ‘golden trio’ in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging from data acquisition to data processing; and from the mathematical background of the analysis to the practical application of processing algorithms. Overall, the approach to the mathematics is informal with a focus on basic understanding of the methods and their interrelationships rather than detailed proofs or derivations. One of the principle goals is to provide the reader with the background required to understand the principles of commercially available analyses software, and to allow him/her to construct his/her own analysis tools in an environment such as MATLAB®. Multiple color illustrations are integrated in the text Includes an introduction to biomedical signals, noise characteristics, and recording techniques Basics and background for more advanced topics can be found in extensive notes and appendices A Companion Website hosts the MATLAB scripts and several data files:

Applied Statistics Using SPSS, STATISTICA and MATLAB

Using Spss, Statistica and Matlab

Author: J. P. Marques de Sá

Publisher: Springer Science & Business Media

ISBN: 9783540011569

Category: Mathematics

Page: 452

View: 1216

Assuming no previous statistics education, this practical reference provides a comprehensive introduction and tutorial on the main statistical analysis topics, demonstrating their solution with the most common software packages. Intended for anyone applying statistical analysis to a wide variety of science and engineering problems, the book shows how to use SPSS, MATLAB, and STATISTICA for data analyses, such as data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics.

Signals and Systems in Biomedical Engineering

Signal Processing and Physiological Systems Modeling

Author: Suresh R. Devasahayam

Publisher: Springer Science & Business Media

ISBN: 9780306463914

Category: Mathematics

Page: 337

View: 4028

CD-ROM includes programs for teaching signal processing in installable form.

Multi-Sensor Data Fusion with MATLAB®

Author: Jitendra R. Raol

Publisher: CRC Press

ISBN: 9781439800058

Category: Technology & Engineering

Page: 568

View: 8169

Using MATLAB® examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion. The authors elucidate DF strategies, algorithms, and performance evaluation mainly for aerospace applications, although the methods can also be applied to systems in other areas, such as biomedicine, military defense, and environmental engineering. After presenting several useful strategies and algorithms for DF and tracking performance, the book evaluates DF algorithms, software, and systems. It next covers fuzzy logic, fuzzy sets and their properties, fuzzy logic operators, fuzzy propositions/rule-based systems, an inference engine, and defuzzification methods. It develops a new MATLAB graphical user interface for evaluating fuzzy implication functions, before using fuzzy logic to estimate the unknown states of a dynamic system by processing sensor data. The book then employs principal component analysis, spatial frequency, and wavelet-based image fusion algorithms for the fusion of image data from sensors. It also presents procedures for combing tracks obtained from imaging sensor and ground-based radar. The final chapters discuss how DF is applied to mobile intelligent autonomous systems and intelligent monitoring systems. Fusing sensors’ data can lead to numerous benefits in a system’s performance. Through real-world examples and the evaluation of algorithmic results, this detailed book provides an understanding of MSDF concepts and methods from a practical point of view. Select MATLAB programs are available for download on

Mathematische Methoden zur Mechanik

Ein Handbuch mit MATLAB®-Experimenten

Author: Eckart W. Gekeler

Publisher: Springer-Verlag

ISBN: 3642142532

Category: Mathematics

Page: 645

View: 5972

Das Spannungsfeld zwischen Mathematik und Mechanik bildet eine nie versiegende Quelle neuer Entwicklungen. Das Lehrbuch enthält eine breite Palette klassischer Themen, vom Dreikörperproblem und der Kreiseltheorie über Bifurkationstheorie und Optimierung bis zur Kontinuumsmechanik elastischer Körper und Flüssigkeiten. Mit Matlab-Programmen, die auf der Homepage des Autors zur Verfügung gestellt werden, können Leser Experimente durchführen. Dabei kann jedes Bild oder Diagramm selbst erzeugt und Daten oder Algorithmus nach Belieben abgeändert werden.

Nonlinear Inverse Problems in Imaging

Author: Jin Keun Seo,Eung Je Woo

Publisher: John Wiley & Sons

ISBN: 1118478150

Category: Technology & Engineering

Page: 376

View: 852

This book provides researchers and engineers in the imaging field with the skills they need to effectively deal with nonlinear inverse problems associated with different imaging modalities, including impedance imaging, optical tomography, elastography, and electrical source imaging. Focusing on numerically implementable methods, the book bridges the gap between theory and applications, helping readers tackle problems in applied mathematics and engineering. Complete, self-contained coverage includes basic concepts, models, computational methods, numerical simulations, examples, and case studies. Provides a step-by-step progressive treatment of topics for ease of understanding. Discusses the underlying physical phenomena as well as implementation details of image reconstruction algorithms as prerequisites for finding solutions to non linear inverse problems with practical significance and value. Includes end of chapter problems, case studies and examples with solutions throughout the book. Companion website will provide further examples and solutions, experimental data sets, open problems, teaching material such as PowerPoint slides and software including MATLAB m files. Essential reading for Graduate students and researchers in imaging science working across the areas of applied mathematics, biomedical engineering, and electrical engineering and specifically those involved in nonlinear imaging techniques, impedance imaging, optical tomography, elastography, and electrical source imaging

Nonparametric Statistics with Applications to Science and Engineering

Author: Paul H. Kvam,Brani Vidakovic

Publisher: John Wiley & Sons

ISBN: 9780470168691

Category: Mathematics

Page: 448

View: 5759

A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to nonparametric statistical analysis and provides comprehensive coverage of both established and newly developed methods. With the use of MATLAB, the authors present information on theorems and rank tests in an applied fashion, with an emphasis on modern methods in regression and curve fitting, bootstrap confidence intervals, splines, wavelets, empirical likelihood, and goodness-of-fit testing. Nonparametric Statistics with Applications to Science and Engineering begins with succinct coverage of basic results for order statistics, methods of categorical data analysis, nonparametric regression, and curve fitting methods. The authors then focus on nonparametric procedures that are becoming more relevant to engineering researchers and practitioners. The important fundamental materials needed to effectively learn and apply the discussed methods are also provided throughout the book. Complete with exercise sets, chapter reviews, and a related Web site that features downloadable MATLAB applications, this book is an essential textbook for graduate courses in engineering and the physical sciences and also serves as a valuable reference for researchers who seek a more comprehensive understanding of modern nonparametric statistical methods.

Partial Differential Equation Analysis in Biomedical Engineering

Case Studies with Matlab

Author: W. E. Schiesser

Publisher: Cambridge University Press

ISBN: 1107022800

Category: Mathematics

Page: 422

View: 4059

Gives graduate students and researchers an introductory overview of partial differential equation analysis of biomedical engineering systems through detailed examples.

Applied Biomechatronics Using Mathematical Models

Author: Jorge Garza Ulloa

Publisher: Academic Press

ISBN: 0128125950

Category: Technology & Engineering

Page: 662

View: 897

Applied Biomechatronics Using Mathematical Models provides an appropriate methodology to detect and measure diseases and injuries relating to human kinematics and kinetics. It features mathematical models that, when applied to engineering principles and techniques in the medical field, can be used in assistive devices that work with bodily signals. The use of data in the kinematics and kinetics analysis of the human body, including musculoskeletal kinetics and joints and their relationship to the central nervous system (CNS) is covered, helping users understand how the complex network of symbiotic systems in the skeletal and muscular system work together to allow movement controlled by the CNS. With the use of appropriate electronic sensors at specific areas connected to bio-instruments, we can obtain enough information to create a mathematical model for assistive devices by analyzing the kinematics and kinetics of the human body. The mathematical models developed in this book can provide more effective devices for use in aiding and improving the function of the body in relation to a variety of injuries and diseases. Focuses on the mathematical modeling of human kinematics and kinetics Teaches users how to obtain faster results with these mathematical models Includes a companion website with additional content that presents MATLAB examples

Statistical and Computational Methods in Brain Image Analysis

Author: Moo K. Chung

Publisher: CRC Press

ISBN: 1439836353

Category: Mathematics

Page: 400

View: 9774

The massive amount of nonstandard high-dimensional brain imaging data being generated is often difficult to analyze using current techniques. This challenge in brain image analysis requires new computational approaches and solutions. But none of the research papers or books in the field describe the quantitative techniques with detailed illustrations of actual imaging data and computer codes. Using MATLAB® and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain how to perform statistical analysis on brain imaging data. The book focuses on methodological issues in analyzing structural brain imaging modalities such as MRI and DTI. Real imaging applications and examples elucidate the concepts and methods. In addition, most of the brain imaging data sets and MATLAB codes are available on the author’s website. By supplying the data and codes, this book enables researchers to start their statistical analyses immediately. Also suitable for graduate students, it provides an understanding of the various statistical and computational methodologies used in the field as well as important and technically challenging topics.

Bootstrap Techniques for Signal Processing

Author: Abdelhak M. Zoubir,D. Robert Iskander

Publisher: Cambridge University Press

ISBN: 9780521831277

Category: Juvenile Nonfiction

Page: 217

View: 4505

This book is about the foundations of the bootstrap, a powerful tool in signal processing, and its properties, strengths, and limitations. Focused on bootstrap signal detection in Gaussian and non-Gaussian interference as well as bootstrap model selection, the theory developed by the authors is supported by practical examples written in MATLAB. Aimed at graduate students and engineers, the book includes applications to problems in radar and sonar, biomedical engineering, and automotive engineering.

Nonnegative Matrix and Tensor Factorizations

Applications to Exploratory Multi-way Data Analysis and Blind Source Separation

Author: Andrzej Cichocki,Rafal Zdunek,Anh Huy Phan,Shun-ichi Amari

Publisher: John Wiley & Sons

ISBN: 9780470747285

Category: Science

Page: 500

View: 5195

This book provides a broad survey of models and efficient algorithms for Nonnegative Matrix Factorization (NMF). This includes NMF’s various extensions and modifications, especially Nonnegative Tensor Factorizations (NTF) and Nonnegative Tucker Decompositions (NTD). NMF/NTF and their extensions are increasingly used as tools in signal and image processing, and data analysis, having garnered interest due to their capability to provide new insights and relevant information about the complex latent relationships in experimental data sets. It is suggested that NMF can provide meaningful components with physical interpretations; for example, in bioinformatics, NMF and its extensions have been successfully applied to gene expression, sequence analysis, the functional characterization of genes, clustering and text mining. As such, the authors focus on the algorithms that are most useful in practice, looking at the fastest, most robust, and suitable for large-scale models. Key features: Acts as a single source reference guide to NMF, collating information that is widely dispersed in current literature, including the authors’ own recently developed techniques in the subject area. Uses generalized cost functions such as Bregman, Alpha and Beta divergences, to present practical implementations of several types of robust algorithms, in particular Multiplicative, Alternating Least Squares, Projected Gradient and Quasi Newton algorithms. Provides a comparative analysis of the different methods in order to identify approximation error and complexity. Includes pseudo codes and optimized MATLAB source codes for almost all algorithms presented in the book. The increasing interest in nonnegative matrix and tensor factorizations, as well as decompositions and sparse representation of data, will ensure that this book is essential reading for engineers, scientists, researchers, industry practitioners and graduate students across signal and image processing; neuroscience; data mining and data analysis; computer science; bioinformatics; speech processing; biomedical engineering; and multimedia.

Signal Processing for Neuroscientists, A Companion Volume

Advanced Topics, Nonlinear Techniques and Multi-Channel Analysis

Author: Wim van Drongelen

Publisher: Elsevier

ISBN: 0123849160

Category: Medical

Page: 186

View: 6736

The popularity of signal processing in neuroscience is increasing, and with the current availability and development of computer hardware and software, it is anticipated that the current growth will continue. Because electrode fabrication has improved and measurement equipment is getting less expensive, electrophysiological measurements with large numbers of channels are now very common. In addition, neuroscience has entered the age of light, and fluorescence measurements are fully integrated into the researcher’s toolkit. Because each image in a movie contains multiple pixels, these measurements are multi-channel by nature. Furthermore, the availability of both generic and specialized software packages for data analysis has altered the neuroscientist’s attitude toward some of the more complex analysis techniques. This book is a companion to the previously published Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals, which introduced readers to the basic concepts. It discusses several advanced techniques, rediscovers methods to describe nonlinear systems, and examines the analysis of multi-channel recordings. Covers the more advanced topics of linear and nonlinear systems analysis and multi-channel analysis Includes practical examples implemented in MATLAB Provides multiple references to the basics to help the student

State-Space Methods for Time Series Analysis

Theory, Applications and Software

Author: Jose Casals,Alfredo Garcia-Hiernaux,Miguel Jerez,Sonia Sotoca,A. Alexandre Trindade

Publisher: CRC Press

ISBN: 131536025X

Category: Mathematics

Page: 270

View: 6272

The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors’ E4 MATLAB® toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.