000 | 10584nam a2201849 i 4500 | ||
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001 | 5959849 | ||
003 | IEEE | ||
005 | 20200421114236.0 | ||
006 | m o d | ||
007 | cr |n||||||||| | ||
008 | 151221s2011 nju ob 001 eng d | ||
020 |
_a9781118007747 _qebook |
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020 |
_z9780470422144 _qprint |
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020 |
_z1118007743 _qelectronic |
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024 | 7 |
_a10.1002/9781118007747 _2doi |
|
035 | _a(CaBNVSL)mat05959849 | ||
035 | _a(IDAMS)0b000064815f34e1 | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
050 | 4 |
_aR857.S47 _bA37 2011eb |
|
082 | 0 | 4 |
_a610.28 _222 |
245 | 0 | 0 |
_aAdvanced methods of biomedical signal processing / _cedited by Sergio Cerutti, Carlo Marchesi. |
264 | 1 |
_aPiscataway, New Jersey : _bIEEE Press, _cc2011. |
|
264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2011] |
|
300 | _a1 PDF (512 pages). | ||
336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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490 | 1 |
_aIEEE press series on biomedical engineering ; _v27 |
|
500 | _aIn Wiley online library | ||
504 | _aIncludes bibliographical references. | ||
505 | 0 | _aPreface -- Contributors -- Part I. Fundamentals of Biomedical Signal Processing and Introduction to Advanced Methods -- 1. Methods of Biomedical Signal Processing -- Multiparametric and Multidisciplinary Integration toward a Better Comprehension of Pathophysiological Mechanisms (Sergio Cerutti) -- 2. Data, Signals, and Information -- Medical Applications of Digital Signal Processing (Carlo Marchesi, Matteo Paoletti, and Loriano Galeotti) -- Part II. Points of View of the Physiologist and Clinician -- 3. Methods and Neurons (Gabriele E. M. Biella) -- 4. Evaluation of the Autonomic Nervous System -- From Algorithms to Clinical Practice (Maria Teresa La Rovere) -- Part III. Models and Biomedical Signals -- 5. Parametric Models for the Analysis of Interactions in Biomedical Signals (Giuseppe Baselli, Alberto Porta, and Paolo Bolzern) -- 6. Use of Interpretative Models in Biological Signal Processing (Mauro Ursino) -- 7. Multimodal Integration of EEG, MEG, and Functional MRI in the Study of Human Brain Activity (Fabio Babiloni, Fabrizio De Vico Fallani, and Febo Cincotti) -- 8. Deconvolution for Physiological Signal Analysis (Giovanni Sparacino, Gianluigi Pillonetto, Giuseppe De Nicolao, and Claudio Cobelli) -- Part IV. Time-Frequency, Time-Scale, and Wavelet Analysis -- 9. Linear Time-Frequency Representation (Maurizio Varanini) -- 10. Quadratic Time-Frequency Representation (Luca Mainardi) -- 11. Time-Variant Spectral Estimation (Anna M. Bianchi) -- Part V. Complexity Analysis and Nonlinear Methods -- 12. Dynamical Systems and Their Bifurcations (Fabio Dercole and Sergio Rinaldi) -- 13. Fractal Dimension -- From Geometry to Physiology (Rita Balocchi) -- 14. Nonlinear Analysis of Experimental Time Series (Maria Gabriella Signorini and Manuela Ferrario) -- 15. Blind Source Separation -- Application to Biomedical Signals (Luca Mesin, Aleš Holobar, and Roberto Merletti) -- 16. Higher Order Spectra (Giovanni Calcagnini and Federica Censi) -- Part VI. Information Processing of Molecular Biology Data. | |
505 | 8 | _a17. Molecular Bioengineering and Nanobioscience -- Data Analysis and Processing Methods (Carmelina Ruggiero) -- 18. Microarray Data Analysis -- General Concepts, Gene Selection, and Classification (Ricardo Bellazzi, Silvio Bicciato, Claudio Cobelli, Barbara Di Camillo, Fulvia Ferraazzi, Paolo Magni, Licia Sacchi, and Gianna Toffolo) -- 19. Microarray Data Analysis -- Gene Regulatory Networks (Riccardo Bellazzi, Silvio Bicciato, Claudio Cobelli, Barbara Di Camillo, Fulvia Ferrazzi, Paolo Magni, Lucia Sacchi, and Gianna Toffolo) -- 20. Biomolecular Sequence Analysis (Linda Pattini and Sergio Cerutti) -- Part VII. Classification and Feature Extraction -- 21. Soft Computing in Signal and Data Analysis -- Neural Networks, Neuro-Fuzzy Networks, and Genetic Algorithms (Giovanni Magenes, Francesco Lunghi, and Stefano Ramat) -- 22. Interpretation and Classification of Patient Status Patterns (Matteo Paoletti and Carlo Marchesi) -- Index -- IEEE Press Series in Biomedical Engineering. | |
506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
520 | _aA complete introduction to the application of advanced signal processing methods to biomedical engineering problemsThis edited volume, which grew out of the GNB (Gruppo Nazionale di Bioingegneria, Italy) Summer School on Biomedical Signal Processing, explains some of the most advanced methodological signal processing techniques and applies them to biomedical engineering problems. Prominent experts in the areas of biomedical signal processing, biomedical data treatment, medicine, signal processing, system biology, and applied physiology introduce novel techniques and algorithms as well as their clinical or physiological applications.Divided into seven sections, Advanced Methods of Biomedical Signal Processing covers:. The peculiarities of biomedical signal processing with respect to more traditional applications of digital signal processing and their classification. An experimental physiologist's and cardiologist's view of the cardiovascular, central and autonomic nervous systems. An important link between biomedical signal processing and physiological modeling. Time-frequency, time-scale, and wavelet analysis. Advanced methods employed in complexity measurements. Computational genomics and proteomics. Key methods for signal classification, such as neural networks, neuro-fuzzy and genetic algorithmsThe book provides a compelling overview of techniques, such as multisource and multi-scale integration of information for physiology and clinical decision; the integration of signal processing methods with a modeling approach; complexity measurement from biomedical signals; higher order analysis in biomedical signals; and classification and parameter enhancement. Various contributions reveal that biomedical signal processing must be viewed in a wider context, with key links to the modeling phase of the signal-generating mechanisms, in order to better comprehend the behavior of the biological system under investigation.Graduate and PhD students in engineering/biomedical engineering courses, physics and applied mathematics, as well as research professionals in medical and biological sciences will highly benefit from this authoritative resource. | ||
530 | _aAlso available in print. | ||
538 | _aMode of access: World Wide Web | ||
588 | _aDescription based on PDF viewed 12/21/2015. | ||
650 | 0 | _aSignal processing. | |
650 | 0 | _aBiomedical engineering. | |
650 | 1 | 2 | _aBiomedical Engineering. |
650 | 1 | 2 | _aSignal Processing, Computer-Assisted. |
650 | 2 | 2 |
_aMicroarray Analysis _xmethods. |
650 | 2 | 2 | _aModels, Biological. |
655 | 0 | _aElectronic books. | |
695 | _aAdaptive systems | ||
695 | _aAdditives | ||
695 | _aAdvertising | ||
695 | _aAnalytical models | ||
695 | _aArrays | ||
695 | _aAutoregressive processes | ||
695 | _aBayesian methods | ||
695 | _aBifurcation | ||
695 | _aBioinformatics | ||
695 | _aBiological neural networks | ||
695 | _aBiological system modeling | ||
695 | _aBiological systems | ||
695 | _aBiomedical engineering | ||
695 | _aBiomedical measurements | ||
695 | _aBrain modeling | ||
695 | _aBrain models | ||
695 | _aCentral nervous system | ||
695 | _aChaos | ||
695 | _aChirp | ||
695 | _aClassification algorithms | ||
695 | _aCoherence | ||
695 | _aCorrelation | ||
695 | _aDNA | ||
695 | _aData analysis | ||
695 | _aData mining | ||
695 | _aData models | ||
695 | _aDeconvolution | ||
695 | _aDifferential equations | ||
695 | _aDigital signal processing | ||
695 | _aElectric potential | ||
695 | _aElectroencephalography | ||
695 | _aEncoding | ||
695 | _aEntropy | ||
695 | _aEquations | ||
695 | _aError probability | ||
695 | _aEstimation | ||
695 | _aExtraterrestrial measurements | ||
695 | _aFabrication | ||
695 | _aFeature extraction | ||
695 | _aFiltering | ||
695 | _aFluorescence | ||
695 | _aFourier transforms | ||
695 | _aFractals | ||
695 | _aFrequency modulation | ||
695 | _aGene expression | ||
695 | _aGenetic algorithms | ||
695 | _aGenomics | ||
695 | _aHeart rate variability | ||
695 | _aHigher order statistics | ||
695 | _aHumans | ||
695 | _aIEEE Press | ||
695 | _aImage reconstruction | ||
695 | _aIndexes | ||
695 | _aIrrigation | ||
695 | _aKernel | ||
695 | _aLeast squares approximation | ||
695 | _aLimit-cycles | ||
695 | _aMagnetic heads | ||
695 | _aManifolds | ||
695 | _aMathematical model | ||
695 | _aMedical diagnostic imaging | ||
695 | _aMicroscopy | ||
695 | _aMutual information | ||
695 | _aMyocardium | ||
695 | _aNanobioscience | ||
695 | _aNeurons | ||
695 | _aNoise | ||
695 | _aNoise measurement | ||
695 | _aOptical fiber sensors | ||
695 | _aOscillators | ||
695 | _aPathology | ||
695 | _aPerformance evaluation | ||
695 | _aPhysiology | ||
695 | _aPlasmas | ||
695 | _aPredictive models | ||
695 | _aPrincipal component analysis | ||
695 | _aProbes | ||
695 | _aProteins | ||
695 | _aRandom variables | ||
695 | _aReverse engineering | ||
695 | _aScalp | ||
695 | _aSections | ||
695 | _aSensors | ||
695 | _aShape | ||
695 | _aSignal processing | ||
695 | _aSignal processing algorithms | ||
695 | _aSignal resolution | ||
695 | _aSkeleton | ||
695 | _aSkull | ||
695 | _aSource separation | ||
695 | _aSpinal cord | ||
695 | _aSupport vector machine classification | ||
695 | _aTaylor series | ||
695 | _aTechnological innovation | ||
695 | _aTime domain analysis | ||
695 | _aTime frequency analysis | ||
695 | _aTime measurement | ||
695 | _aTime series analysis | ||
695 | _aTopology | ||
695 | _aTrajectory | ||
695 | _aTransforms | ||
695 | _aUncertainty | ||
695 | _aWavelet analysis | ||
695 | _aWavelet transforms | ||
700 | 1 | _aCerutti, Sergio. | |
700 | 1 | _aMarchesi, Carlo. | |
710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. |
|
710 | 2 |
_aJohn Wiley & Sons, _epublisher. |
|
776 | 0 | 8 |
_iPrint version: _z9780470422144 |
830 | 0 |
_aIEEE Press series in biomedical engineering ; _v27 |
|
856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5959849 |
942 | _cEBK | ||
999 |
_c59778 _d59778 |