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A biomedical engineering perspective on the theory, methods, and applications of signal processing This book provides a unique framework for understanding signal processing of biomedical signals and what it tells us about signal sources and their behavior in response to perturbation. Using a modeling-based approach, the author shows how to perform signal processing by developing and manipulating a model of the signal source, providing a logical, coherent basis for recognizing signal types and for tackling the special challenges posed by biomedical signals-including the effects of noise on the signal, changes in basic properties, or the fact that these signals contain large stochastic components and may even be fractal or chaotic. Each chapter begins with a detailed biomedical example, illustrating the methods under discussion and highlighting the interconnection between the theoretical concepts and applications. The author has enlisted experts from numerous subspecialties in biomedical engineering to help develop these examples and has made most examples available as Matlab or Simulink files via anonymous ftp. Without the need for a background in electrical engineering, readers will become acquainted with proven techniques for analyzing biomedical signals and learn how to choose the appropriate method for a given application.

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The development of techniques to analyze biomedical signals, such as electro-cardiograms, has dramatically affected countless lives by making possible improved noninvasive diagnosis, online monitoring of critically ill patients, and rehabilitation and sensory aids for the handicapped. Rangaraj Rangayyan supplies a practical, hands-on field guide to this constantly evolving technology in Biomedical Signal Analysis, focusing on the diagnostic challenges that medical professionals continue to face. Dr. Rangayyan applies a problem-solving approach to his study. Each chapter begins with the statement of a different biomedical signal problem, followed by a selection of real-life case studies and the associated signals. Signal processing, modeling, or analysis techniques are then presented, starting with relatively simple "textbook" methods, followed by more sophisticated research approaches. The chapter concludes with one or more application solutions; illustrations of real-life biomedical signals and their derivatives are included throughout. Among the topics addressed are: . Concurrent, coupled, and correlated processes. Filtering for removal of artifacts. Event detection and characterization. Frequency-domain characterization. Modeling biomedical systems. Analysis of nonstationary signals. Pattern classification and diagnostic decision The chapters also present a number of laboratory exercises, study questions, and problems to facilitate preparation for class examinations and practical applications. Biomedical Signal Analysis provides a definitive resource for upper-level under-graduate and graduate engineering students, as well as for practicing engineers, computer scientists, information technologists, medical physicists, and data processing specialists. An authoritative assessment of the problems and applications of biomedical signals, rooted in practical case studies An Instructor Support FTP site is available from the Wiley editorial department: ftp://ftp.ieee.org/uploads/press/rangayyan.

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"Written for graduate students and professionals dealing with heart rate variability (HRV), this cutting-edge reference reviews how minute variations in the beat-to-beat heart rate are regulated. It explores how these variations can be used as a window to understanding the central and peripheral mechanisms that modulate the autonomic nervous systems. Explaining how HRV is characterized through simple statistics and frequency analysis in both healthy human subjects and patients with a variety of diseases, the book provides examples for methods that require mathematical techniques. The authors cite a variety of real-life medical situations and offer extensive end-of-chapter references"--Provided by publisher.

Biomathematics. Biometry. Biostatistics --- Blood physiology. Circulatory physiology --- signaalanalyse --- hartbewaking --- Heart Rate --- Autonomic Nervous System --- Data Interpretation, Statistical --- Heart Function Tests --- Signal Processing, Computer-Assisted --- Digital Signal Processing --- Signal Interpretation, Computer-Assisted --- Signal Processing, Digital --- Computer-Assisted Signal Interpretation --- Computer-Assisted Signal Interpretations --- Computer-Assisted Signal Processing --- Interpretation, Computer-Assisted Signal --- Interpretations, Computer-Assisted Signal --- Signal Interpretation, Computer Assisted --- Signal Interpretations, Computer-Assisted --- Signal Processing, Computer Assisted --- Fetal Monitoring --- Monitoring, Physiologic --- Data Compression --- Cardiac Function Tests --- Cardiac Function Test --- Function Test, Cardiac --- Function Test, Heart --- Function Tests, Cardiac --- Function Tests, Heart --- Heart Function Test --- Test, Cardiac Function --- Test, Heart Function --- Tests, Cardiac Function --- Tests, Heart Function --- Ventricular Function --- Data Interpretations, Statistical --- Interpretation, Statistical Data --- Statistical Data Analysis --- Statistical Data Interpretation --- Data Analysis, Statistical --- Analyses, Statistical Data --- Analysis, Statistical Data --- Data Analyses, Statistical --- Interpretations, Statistical Data --- Statistical Data Analyses --- Statistical Data Interpretations --- physiology

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All of the biomedical measurement technologies, which are now instrumental to the medical field, are essentially useless without proper signal and image processing. Biomedical Signal and Image Processing is unique in providing a comprehensive survey of all the conventional and advanced imaging modalities and the main computational methods used for processing the data obtained from each.This book offers self-contained coverage of the mathematics and biology/physiology necessary to build effective algorithms and programs for biomedical signal and image processing applications. The first part of the book details the main signal and image processing, pattern recognition, and feature extraction techniques along with computational methods from other fields such as information theory and stochastic processes. Building on this foundation, the second part explores the major one-dimensional biological signals, the biological origin and importance of each signal, and the commonly used processing techniques with an emphasis on physiology and diagnostic applications, while the third section does the same for imaging modalities.Throughout the book, the authors rely on practical examples using real data from biomedical systems. They supply several programming examples in MATLAB® to provide hands-on experience and insightIntegrating all major modalities and computational techniques in a single source, Biomedical Signal and Image Processing is a perfect introduction to the field as well as an ideal reference for the established professional.

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This book focuses on advanced techniques used for feature extraction, analysis, recognition, and classification in the area of biomedical signal and image processing. Contributions cover all aspects of artificial intelligence, machine learning, and deep learning in the field of biomedical signal and image processing using novel and unexplored techniques and methodologies. The book covers recent developments in both medical images and signals analyzed by artificial intelligence techniques. The authors also cover topics related to development based artificial intelligence, which includes machine learning, neural networks, and deep learning. This book will provide a platform for researchers who are working in the area of artificial intelligence for biomedical applications. Provides insights into medical signal and image analysis using artificial intelligence; Includes novel and recent trends of decision support system for medical research; Outlines employment of evolutionary algorithms for biomedical data, big data analysis for medical databases, and reliability, opportunities, and challenges in clinical data.

Artificial intelligence --- Imaging systems in medicine. --- Medical applications. --- Medical imaging systems --- Medical instruments and apparatus --- Medicine --- Data processing --- Artificial Intelligence. --- Diagnostic Imaging --- Image Processing, Computer-Assisted. --- Signal Processing, Computer-Assisted. --- methods. --- Digital Signal Processing --- Signal Interpretation, Computer-Assisted --- Signal Processing, Digital --- Computer-Assisted Signal Interpretation --- Computer-Assisted Signal Interpretations --- Computer-Assisted Signal Processing --- Interpretation, Computer-Assisted Signal --- Interpretations, Computer-Assisted Signal --- Signal Interpretation, Computer Assisted --- Signal Interpretations, Computer-Assisted --- Signal Processing, Computer Assisted --- Fetal Monitoring --- Monitoring, Physiologic --- Data Compression --- Analysis, Computer-Assisted Image --- Computer-Assisted Image Analysis --- Biomedical Image Processing --- Computer-Assisted Image Processing --- Digital Image Processing --- Image Analysis, Computer-Assisted --- Image Reconstruction --- Medical Image Processing --- Computer Assisted Image Analysis --- Computer Assisted Image Processing --- Computer-Assisted Image Analyses --- Image Analyses, Computer-Assisted --- Image Analysis, Computer Assisted --- Image Processing, Biomedical --- Image Processing, Computer Assisted --- Image Processing, Digital --- Image Processing, Medical --- Image Processings, Medical --- Image Reconstructions --- Medical Image Processings --- Processing, Biomedical Image --- Processing, Digital Image --- Processing, Medical Image --- Processings, Digital Image --- Processings, Medical Image --- Reconstruction, Image --- Reconstructions, Image --- Multimodal Imaging --- Computational Intelligence --- AI (Artificial Intelligence) --- Computer Reasoning --- Computer Vision Systems --- Knowledge Acquisition (Computer) --- Knowledge Representation (Computer) --- Machine Intelligence --- Acquisition, Knowledge (Computer) --- Computer Vision System --- Intelligence, Artificial --- Intelligence, Computational --- Intelligence, Machine --- Knowledge Representations (Computer) --- Reasoning, Computer --- Representation, Knowledge (Computer) --- System, Computer Vision --- Systems, Computer Vision --- Vision System, Computer --- Vision Systems, Computer --- Heuristics