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semmlow john l.; griffel benjamin - biosignal and medical image processing
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Biosignal and Medical Image Processing

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Dettagli

Genere:Libro
Lingua: Inglese
Editore:

CRC Press

Pubblicazione: 03/2014
Edizione: Edizione nuova, 3° edizione





Note Editore

Written specifically for biomedical engineers, Biosignal and Medical Image Processing, Third Edition provides a complete set of signal and image processing tools, including diagnostic decision-making tools, and classification methods. Thoroughly revised and updated, it supplies important new material on nonlinear methods for describing and classifying signals, including entropy-based methods and scaling methods. A full set of PowerPoint slides covering the material in each chapter and problem solutions is available to instructors for download. See What’s New in the Third Edition: Two new chapters on nonlinear methods for describing and classifying signals. Additional examples with biological data such as EEG, ECG, respiration and heart rate variability Nearly double the number of end-of-chapter problems MATLAB® incorporated throughout the text Data "cleaning" methods commonly used in such areas as heart rate variability studies The text provides a general understanding of image processing sufficient to allow intelligent application of the concepts, including a description of the underlying mathematical principals when needed. Throughout this textbook, signal and image processing concepts are implemented using the MATLAB® software package and several of its toolboxes. The challenge of covering a broad range of topics at a useful, working depth is motivated by current trends in biomedical engineering education, particularly at the graduate level where a comprehensive education must be attained with a minimum number of courses. This has led to the development of "core" courses to be taken by all students. This text was written for just such a core course. It is also suitable for an upper-level undergraduate course and would also be of value for students in other disciplines that would benefit from a working knowledge of signal and image processing.




Sommario

IntroductionBiosignalsBiosignal Measurement SystemsTransducersAmplifier/DetectorAnalog Signal Processing and FiltersADC ConversionData BanksSummaryProblemsBiosignal Measurements, Noise, and AnalysisBiosignalsNoiseSignal Analysis: Data Functions and TransformsSummaryProblemsSpectral Analysis: Classical MethodsIntroductionFourier Series AnalysisPower SpectrumSpectral Averaging: Welch’s MethodSummaryProblemsNoise Reduction and Digital FiltersNoise ReductionNoise Reduction through Ensemble AveragingZ-TransformFinite Impulse Response FiltersInfinite Impulse Response FiltersSummaryProblemsModern Spectral Analysis: The Search for Narrowband SignalsParametric MethodsNonparametric Analysis: Eigenanalysis Frequency EstimationProblemsTimeFrequency AnalysisBasic ApproachesThe Short-Term Fourier Transform: The SpectrogramThe WignerVille Distribution: A Special Case of Cohen’s ClassCohen’s Class DistributionsSummaryProblemsWavelet AnalysisIntroductionContinuous Wavelet TransformDiscrete Wavelet TransformFeature Detection: Wavelet PacketsSummaryProblemsOptimal and Adaptive FiltersOptimal Signal Processing: Wiener Filters8.2 Adaptive Signal Processing8.3 Phase-Sensitive Detection8.4 SummaryProblemsMultivariate Analyses: Principal Component Analysis and Independent Component AnalysisIntroduction: Linear TransformationsPrincipal Component AnalysisIndependent Component AnalysisSummaryProblemsChaos and Nonlinear DynamicsNonlinear SystemsPhase SpaceEstimating the Embedding ParametersQuantifying Trajectories in Phase Space: The Lyapunov ExponentNonlinear Analysis: The Correlation DimensionTests for Nonlinearity: Surrogate Data AnalysisSummaryExercisesNonlinearity Detection: Information-Based MethodsInformation and RegularityMutual Information FunctionSpectral EntropyPhase-Space-Based Entropy MethodsDetrended Fluctuation AnalysisSummaryProblemsFundamentals of Image Processing: The MATLAB Image Processing ToolboxImage-Processing Basics: MATLAB Image FormatsImage DisplayImage Storage and RetrievalBasic Arithmetic OperationsBlock-Processing OperationsSummaryProblemsImage Processing: Filters, Transformations, and RegistrationTwo-Dimensional Fourier TransformLinear FilteringSpatial TransformationsImage RegistrationSummaryProblemsImage SegmentationIntroductionPixel-Based MethodsContinuity-Based MethodsMultithresholdingMorphological OperationsEdge-Based SegmentationSummaryProblemsImage Acquisition and ReconstructionImaging ModalitiesCT, PET, and SPECTMagnetic Resonance ImagingFunctional MRISummaryProblemsClassification I: Linear Discriminant Analysis and Support Vector MachinesIntroductionLinear DiscriminatorsEvaluating Classifier PerformanceHigher Dimensions: Kernel MachinesSupport Vector MachinesMachine Capacity: Overfitting or “Less Is More"Extending the Number of Variables and ClassesCluster AnalysisSummaryProblemsClassification II: Adaptive Neural NetsIntroductionTraining the McCulloughPitts NeuronThe Gradient Decent Method or Delta RuleTwo-Layer Nets: Back ProjectionThree-Layer NetsTraining StrategiesMultiple ClassificationsMultiple Input VariablesSummaryProblemsAppendix A: Numerical Integration in MATLABAppendix B: Useful MATLAB FunctionsBibliographyIndex










Altre Informazioni

ISBN:

9781466567368

Condizione: Nuovo
Dimensioni: 10 x 7 in Ø 2.92 lb
Formato: Copertina rigida
Illustration Notes:333 b/w images, 21 tables and 301
Pagine Arabe: 630


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