|Author||: Kristina M. Ropella|
|Publisher||: Morgan & Claypool Publishers|
|Release Date||: 2007|
|ISBN 10||: 1598291963|
|Pages||: 93 pages|
There are many books written about statistics, some brief, some detailed, some humorous, some colorful, and some quite dry. Each of these texts is designed for a specific audience. Too often, texts about statistics have been rather theoretical and intimidating for those not practicing statistical analysis on a routine basis. Thus, many engineers and scientists, who need to use statistics much more frequently than calculus or differential equations, lack sufficient knowledge of the use of statistics. The audience that is addressed in this text is the university-level biomedical engineering student who needs a bare-bones coverage of the most basic statistical analysis frequently used in biomedical engineering practice. The text introduces students to the essential vocabulary and basic concepts of probability and statistics that are required to perform the numerical summary and statistical analysis used in the biomedical field. This text is considered a starting point for important issues to consider when designing experiments, summarizing data, assuming a probability model for the data, testing hypotheses, and drawing conclusions from sampled data.A student who has completed this text should have sufficient vocabulary to read more advanced texts on statistics and further their knowledge about additional numerical analyses that are used in the biomedical engineering field but are beyond the scope of this text. This book is designed to supplement an undergraduate-level course in applied statistics, specifically in biomedical engineering. Practicing engineers who have not had formal instruction in statistics may also use this text as a simple, brief introduction to statistics used in biomedical engineering. The emphasis is on the application of statistics, the assumptions made in applying the statistical tests, the limitations of these elementary statistical methods, and the errors often committed in using statistical analysis. A number of examples from biomedical engineering research and industry practice are provided to assist the reader in understanding concepts and application. It is beneficial for the reader to have some background in the life sciences and physiology and to be familiar with basic biomedical instrumentation used in the clinical environment.Contents: Introduction / Collecting Data and Experimental Design / Data Summary and Descriptive Statistics / Assuming a Probability Model from the Sample Data / Statistical Inference / Linear Regression and Correlation Analysis / Power Analysis and Sample Size / Just the Beginning / Bibliography
|Author||: Andrew King,Paul Aljabar|
|Publisher||: Academic Press|
|Release Date||: 2017-06-14|
|ISBN 10||: 0128135107|
|Pages||: 324 pages|
MATLAB Programming for Biomedical Engineers and Scientists provides an easy-to-learn introduction to the fundamentals of computer programming in MATLAB. This book explains the principles of good programming practice, while demonstrating how to write efficient and robust code that analyzes and visualizes biomedical data. Aimed at the biomedical engineer, biomedical scientist, and medical researcher with little or no computer programming experience, it is an excellent resource for learning the principles and practice of computer programming using MATLAB. This book enables the reader to: Analyze problems and apply structured design methods to produce elegant, efficient and well-structured program designs Implement a structured program design in MATLAB, making good use of incremental development approaches Write code that makes good use of MATLAB programming features, including control structures, functions and advanced data types Write MATLAB code to read in medical data from files and write data to files Write MATLAB code that is efficient and robust to errors in input data Write MATLAB code to analyze and visualize medical data, including imaging data For a firsthand interview with the authors, please visit http://scitechconnect.elsevier.com/matlab-programming-biomedical-engineers-scientists/ To access student materials, please visit https://www.elsevier.com/books-and-journals/book-companion/9780128122037 To register and access instructor materials, please visit http://textbooks.elsevier.com/web/Manuals.aspx?isbn=9780128122037 Many real world biomedical problems and data show the practical application of programming concepts Two whole chapters dedicated to the practicalities of designing and implementing more complex programs An accompanying website containing freely available data and source code for the practical code examples, activities, and exercises in the book For instructors, there are extra teaching materials including a complete set of slides, notes for a course based on the book, and course work suggestions
|Author||: Kun Chang Lee,Sanjiban Sekhar Roy,Pijush Samui,Vijay Kumar|
|Publisher||: Academic Press|
|Release Date||: 2020-10-23|
|ISBN 10||: 0128193158|
|Pages||: 292 pages|
Data Analytics in Biomedical Engineering and Healthcare explores key applications using data analytics, machine learning, and deep learning in health sciences and biomedical data. The book is useful for those working with big data analytics in biomedical research, medical industries, and medical research scientists. The book covers health analytics, data science, and machine and deep learning applications for biomedical data, covering areas such as predictive health analysis, electronic health records, medical image analysis, computational drug discovery, and genome structure prediction using predictive modeling. Case studies demonstrate big data applications in healthcare using the MapReduce and Hadoop frameworks. Examines the development and application of data analytics applications in biomedical data Presents innovative classification and regression models for predicting various diseases Discusses genome structure prediction using predictive modeling Shows readers how to develop clinical decision support systems Shows researchers and specialists how to use hybrid learning for better medical diagnosis, including case studies of healthcare applications using the MapReduce and Hadoop frameworks
Over the last few decades, there are increasing public awareness of adverse events involving engineering failures that not only led to monetary losses but also more importantly, human injuries and deaths. Whilst it is vital for an engineering professional or student to acquire the necessary technical knowledge and skills in their respective field, they must also understand the ethical essences that are relevant to their profession. Engineering professionals like biomedical engineers, need to appreciate the fundamentals of best practices and recognise how any derivation from such practices can have undesirable impacts on human lives. Through this book, it is hoped that readers would draw the relevance between the study of ethics and biomedical engineering. The book would be a useful source and reference for college-level and university-level students. Moreover, the contents are written so as to also provide valuable insights even for existing biomedical engineers and those enrolled in continual engineering education programs.
Numerical Modeling in Biomedical Engineering brings together the integrative set of computational problem solving tools important to biomedical engineers. Through the use of comprehensive homework exercises, relevant examples and extensive case studies, this book integrates principles and techniques of numerical analysis. Covering biomechanical phenomena and physiologic, cell and molecular systems, this is an essential tool for students and all those studying biomedical transport, biomedical thermodynamics & kinetics and biomechanics. Supported by Whitaker Foundation Teaching Materials Program; ABET-oriented pedagogical layout Extensive hands-on homework exercises
|Author||: Valentina E. Balas,Vijender Kumar Solanki,Raghvendra Kumar,Manju Khari|
|Publisher||: Academic Press|
|Release Date||: 2019-11-13|
|ISBN 10||: 0128183195|
|Pages||: 318 pages|
Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more
|Author||: Richard Shiavi|
|Release Date||: 2010-07-19|
|ISBN 10||: 0080467687|
|Pages||: 424 pages|
Introduction to Applied Statistical Signal Analysis, Third Edition, is designed for the experienced individual with a basic background in mathematics, science, and computer. With this predisposed knowledge, the reader will coast through the practical introduction and move on to signal analysis techniques, commonly used in a broad range of engineering areas such as biomedical engineering, communications, geophysics, and speech. Topics presented include mathematical bases, requirements for estimation, and detailed quantitative examples for implementing techniques for classical signal analysis. This book includes over one hundred worked problems and real world applications. Many of the examples and exercises use measured signals, most of which are from the biomedical domain. The presentation style is designed for the upper level undergraduate or graduate student who needs a theoretical introduction to the basic principles of statistical modeling and the knowledge to implement them practically. Includes over one hundred worked problems and real world applications. Many of the examples and exercises in the book use measured signals, many from the biomedical domain.
This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling, and statistical network analysis. Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis Offers programming examples and datasets Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material Features supplementary materials, including datasets, links, and a statistical package available online Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before.
Biomedical Engineering: Health Care Systems, Technology and Techniques is an edited volume with contributions from world experts. It provides readers with unique contributions related to current research and future healthcare systems. Practitioners and researchers focused on computer science, bioinformatics, engineering and medicine will find this book a valuable reference.
Medical Physics and Biomedical Engineering provides broad coverage appropriate for senior undergraduates and graduates in medical physics and biomedical engineering. Divided into two parts, the first part presents the underlying physics, electronics, anatomy, and physiology and the second part addresses practical applications. The structured approach means that later chapters build and broaden the material introduced in the opening chapters; for example, students can read chapters covering the introductory science of an area and then study the practical application of the topic. Coverage includes biomechanics; ionizing and nonionizing radiation and measurements; image formation techniques, processing, and analysis; safety issues; biomedical devices; mathematical and statistical techniques; physiological signals and responses; and respiratory and cardiovascular function and measurement. Where necessary, the authors provide references to the mathematical background and keep detailed derivations to a minimum. They give comprehensive references to junior undergraduate texts in physics, electronics, and life sciences in the bibliographies at the end of each chapter.
Internet of Things in Biomedical Engineering presents the most current research in Internet of Things (IoT) applications for clinical patient monitoring and treatment. The book takes a systems-level approach for both human-factors and the technical aspects of networking, databases and privacy. Sections delve into the latest advances and cutting-edge technologies, starting with an overview of the Internet of Things and biomedical engineering, as well as a focus on ‘daily life.’ Contributors from various experts then discuss ‘computer assisted anthropology,’ CLOUDFALL, and image guided surgery, as well as bio-informatics and data mining. This comprehensive coverage of the industry and technology is a perfect resource for students and researchers interested in the topic. Presents recent advances in IoT for biomedical engineering, covering biometrics, bioinformatics, artificial intelligence, computer vision and various network applications Discusses big data and data mining in healthcare and other IoT based biomedical data analysis Includes discussions on a variety of IoT applications and medical information systems Includes case studies and applications, as well as examples on how to automate data analysis with Perl R in IoT
|Author||: Rodrigo Rojas Moraleda,Nektarios A. Valous,Wei Xiong,Niels Halama|
|Publisher||: CRC Press|
|Release Date||: 2019-07-12|
|ISBN 10||: 0429810989|
|Pages||: 116 pages|
This book provides an accessible yet rigorous introduction to topology and homology focused on the simplicial space. It presents a compact pipeline from the foundations of topology to biomedical applications. It will be of interest to medical physicists, computer scientists, and engineers, as well as undergraduate and graduate students interested in this topic. Features: Presents a practical guide to algebraic topology as well as persistence homology Contains application examples in the field of biomedicine, including the analysis of histological images and point cloud data