rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Practical biomedical signal analysis using MATLAB
  name:
    name part: Blinowska-Cieślak, Katarzyna J.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Zygierewicz, J. (Jaroslaw)
    role:
      role term: author.
  name:
    name part: Taylor & Francis
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    date issued: 2022
    edition: Second edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: "Fully updated and with exclusive new content, this second edition presents a coherent treatment of various signal processing methods and applications. The book not only covers the current techniques of biomedical signal processing, but it also offers guidance on which methods are appropriate for a given task and different types of data"--
  table of contents: Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- About the Series -- Preface -- List of Abbreviations -- 1. A Short Introduction to MATLAB® -- 1.1. Introduction -- 1.2. Where Is Help? -- 1.3. Vectors and Matrixes -- 1.4. Matrix Operations -- 1.4.1. Algebraic Operations -- 1.4.2. Matrix Indexing -- 1.4.3. Logical Indexing -- 1.4.4. Example Exercise -- 1.5. Conditionals -- 1.6. Loops -- 1.7. Scripts and Functions -- 1.8. Working with Binary Files -- 1.8.1. Saving to and Loading from Binary Files -- 1.8.2. Saving and Loading Signals Using .mat Files -- 1.8.3. Exercises -- 1.8.3.1. Unknown Data Type -- 1.8.3.2. Unknown Number of Channels -- 2. Introductory Concepts -- 2.1. Stochastic and Deterministic Signals, Concepts of Stationarity and Ergodicity -- 2.2. Discrete Signals -- 2.2.1. The Sampling Theorem -- 2.2.1.1. Aliasing -- 2.2.2. Quantization Error -- 2.3. Linear Time Invariant Systems -- 2.4. Duality of Time and Frequency Domains -- 2.4.1. Continuous Periodic Signal -- 2.4.2. Infinite Continuous Signal -- 2.4.3. Finite Discrete Signal -- 2.4.4. Basic Properties of Fourier Transform -- 2.4.5. Power Spectrum: The Plancherel Theorem and Parseval's Theorem -- 2.4.6. Z-Transform -- 2.4.7. Uncertainty Principle -- 2.5. Hypotheses Testing -- 2.5.1. The Null and Alternative Hypothesis -- 2.5.2. Types of Tests -- 2.5.3. Multiple Comparisons Problem -- 2.5.3.1. Correcting the Signi cance Level -- 2.5.3.2. Parametric and Non-Parametric Statistical Maps -- 2.5.3.3. False Discovery Rate -- 2.6. Surrogate Data Techniques -- 3. Single Channel (Univariate) Signal -- 3.1. Filters -- 3.1.1. Designing Filters -- 3.1.2. Changing the Sampling Frequency -- 3.1.3. Matched Filters -- 3.1.4. Wiener Filter -- 3.2. Probabilistic Models -- 3.2.1. Hidden Markov Model -- 3.2.2. Kalman Filters -- 3.3. Stationary Signals.
  table of contents: 3.3.1. Analytic Tools in the Time Domain -- 3.3.1.1. Mean Value, Amplitude Distributions -- 3.3.1.2. Entropy and Information Measure -- 3.3.1.3. Autocorrelation Function -- 3.3.2. Analytic Tools in the Frequency Domain -- 3.3.2.1. Estimators of Spectral Power Density Based on Fourier Transform -- 3.3.2.2. Choice of Windowing Function -- 3.3.2.3. Parametric Models: AR, ARMA -- 3.4. Non-Stationary Signals -- 3.4.1 Instantaneous Amplitude and Instantaneous Frequency -- 3.4.2. Analytic Tools in the Time-Frequency Domain -- 3.4.2.1. Time-Frequency Energy Distributions -- 3.4.2.2. Time-Frequency Signal Decompositions -- 3.4.3. Cross-Frequency Coupling -- 3.4.3.1. Models of Phase-Amplitude Coupling -- 3.4.3.2. Evaluation of Phase-Amplitude Coupling -- 3.5. Non-Linear Methods of Signal Analysis -- 3.5.1. Lyapunov Exponent -- 3.5.2. Correlation Dimension -- 3.5.3. Detrended Fluctuation Analysis -- 3.5.4. Recurrence Plots -- 3.5.5. Poincare Map -- 3.5.6. Approximate, Sample, and Multiscale Entropy -- 3.5.7. Limitations of Non-Linear Methods -- 4. Multiple Channels (Multivariate) Signals -- 4.1. Cross-Estimators: Cross-Correlation, Cross-Spectra, Coherence -- 4.2. Multivariate Autoregressive Model (MVAR) -- 4.2.1. Formulation of MVAR Model -- 4.2.2. MVAR in the Frequency Domain -- 4.3. Measures of Directedness -- 4.3.1. Estimators Based on the Phase Difference -- 4.3.2. Causality Measures -- 4.3.2.1. Granger Causality -- 4.3.2.2. Granger Causality Index and Granger-Geweke Causality -- 4.3.2.3. Directed Transfer Function -- 4.3.2.4. Partial Directed Coherence -- 4.3.2.5. Directed Coherence -- 4.4. Non-Linear Estimators of Dependencies between Signals -- 4.4.1. Kullback-Leibler Entropy, Mutual Information -- 4.4.2. Transfer Entropy -- 4.4.3. Generalized Synchronization and Synchronization Likelihood -- 4.4.4. Phase Synchronization (Phase Locking Value).
  table of contents: 4.4.5. Testing the Reliability of the Estimators of Directedness -- 4.5. Comparison of the Multichannel Estimators of Coupling between Time Series -- 4.5.1. Bivariate versus Multivariate Connectivity Estimators -- 4.5.2. Linear versus Non-Linear Estimators of Connectivity -- 4.5.3. The Measures of Directedness -- 4.6. Multivariate Signal Decompositions -- 4.6.1. Principal Component Analysis (PCA) -- 4.6.1.1. Definition -- 4.6.1.2. Computation -- 4.6.1.3. Possible Applications -- 4.6.2. Independent Components Analysis (ICA) -- 4.6.2.1. Definition -- 4.6.2.2. Estimation of ICA -- 4.6.2.3. Computation -- 4.6.2.4. Possible Applications -- 4.6.3. Common Spatial Patterns -- 4.6.4. Multivariate Matching Pursuit (MMP) -- 5. Application to Biomedical Signals -- 5.1. Brain Signals -- 5.1.1. Generation of Brain Signals -- 5.1.2. EEG/MEG Rhythms -- 5.1.3. EEG Measurement, Electrode Systems -- 5.1.4. MEG Measurement, Sensor Systems -- 5.1.5. Elimination of Artifacts -- 5.1.6. Analysis of Continuous EEG Signals -- 5.1.6.1. Single Channel Analysis -- 5.1.6.2. Mapping -- 5.1.6.3. Connectivity Analysis of Brain Signals -- 5.1.6.4. In uence of Volume Conduction on Connectivity Measures -- 5.1.6.5. Graph Theoretical Analysis -- 5.1.6.6. Sleep EEG Analysis -- 5.1.6.7. Analysis of EEG in Epilepsy -- 5.1.6.8. EEG in Monitoring and Anesthesia -- 5.1.7. Analysis of Epoched EEG Signals -- 5.1.7.1. Analysis of Phase-Locked Responses -- 5.1.7.2. In Pursuit of Single Trial Evoked Responses -- 5.1.7.3. Applications of Cross-Frequency Coupling -- 5.1.7.4. Analysis of Non-Phase-Locked Responses -- 5.1.7.5. Analysis of EEG for Applications in Brain-Computer Interfaces -- 5.1.8. fMRI Derived Time Series -- 5.1.8.1. Relation between EEG and fMRI -- 5.1.9. Near-Infrared Spectroscopy Signals -- 5.2. Heart Signals -- 5.2.1. Electrocardiogram -- 5.2.1.1. Measurement Standards.
  table of contents: 5.2.1.2. Physiological Background and Clinical Applications -- 5.2.1.3. Processing of ECG -- 5.2.2. Heart Rate Variability -- 5.2.2.1. Time-Domain Methods of HRV Analysis -- 5.2.2.2. Frequency-Domain Methods of HRV Analysis -- 5.2.2.3. Non-Linear Methods of HRV Analysis -- 5.2.3. Fetal ECG -- 5.2.4. Magnetocardiogram and Fetal Magnetocardiogram -- 5.2.4.1. Magnetocardiogram -- 5.2.4.2. Fetal MCG -- 5.2.5. Ballistocardiogram, Seismocardiogram, Photoplethysmogram -- 5.2.5.1. Wearable Devices -- 5.3. Electromyogram -- 5.3.1. Measurement Techniques and Physiological Background -- 5.3.2. Quantification of EMG Features -- 5.3.3. Decomposition of Needle EMG -- 5.3.4. Surface EMG -- 5.3.4.1. Surface EMG Decomposition -- 5.4. Acoustic Signals -- 5.4.1. Phonocardiogram -- 5.4.2. Otoacoustic Emissions -- 5.5. Multimodal Analysis of Biomedical Signals -- Bibliography -- Index.
  note: Katarzyna J. Blinowska, Blinowska Zygierewicz.
  note: Includes bibliographical references and index.
  note: K. J. Blinowskais a Professor emeritus at the University of Warsaw, Poland, where she was director of Graduate Studies in Biomedical Physics and head of the Department of Biomedical Physics. Currently, she is employed at the Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. She has been at the forefront in developing new advanced time-series methods for research and clinical applications. J. Żygierewiczis a Professor at the University of Warsaw, Poland. His research focuses on developing methods for analyzing EEG and MEG signals, brain-computer interfaces, applications of machine learning in signal processing and classification.
  subject:
    topic: Electrophysiology
    topic: Mathematics
  subject:
    topic: Signal processing
  subject:
    topic: Électrophysiologie
    topic: Mathématiques
  subject:
    topic: Traitement du signal
  subject:
    topic: MEDICAL
    topic: Biotechnology
  subject:
    topic: SCIENCE
    topic: Physics
  subject:
    topic: TECHNOLOGY
    topic: Electricity
  subject:
    topic: Electrophysiology
    topic: Mathematics
  subject:
    topic: Signal processing
  classification: QP341 .B5949 2022eb
  classification: 612/.01427
  related item:
    title info:
      title: Practical biomedical signal analysis using MATLAB
    name:
      name part: Blinowska-Cieślak, Katarzyna J.
    origin info:
      publisher: Boca Raton : CRC Press, 2022
      edition: Second edition.
    identifier: (DLC) 2021023349
    identifier: (OCoLC)1252739894
  related item:
    title info:
      title: Series in medical physics and biomedical engineering
  identifier: 9780429431357
  identifier: 042943135X
  identifier: 9780429775727
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  identifier: 1032105526
  identifier: 9780429431357 Taylor & Francis
  identifier: 9780429775727 O'Reilly Media
  identifier: http://proxy.library.tamu.edu/login
  identifier: https://go.oreilly.com/TAMU/library/view/-/9780429775727/
  record info:
    record content source: TYFRS
    record creation date: 211007
    record change date: 20241108170130.8
    record identifier: in00005523761
    language of cataloging:
      language term: eng