Biomedical signal analysis : a case-study approach /
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| Author / Creator: | Rangayyan, Rangaraj M. |
|---|---|
| Imprint: | [Piscataway, NJ] : IEEE Press ; [New York] : Wiley-Interscience, c2002. |
| Description: | xxxv, 516 p. : ill., port. |
| Language: | English |
| Series: | IEEE Press series in biomedical engineering |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/4619106 |
Table of Contents:
- Dedication
- Preface
- About the Author
- Acknowledgments
- Symbols and Abbreviations
- 1. Introduction to Biomedical Signals
- 1.1. The Nature of Biomedical Signals
- 1.2. Examples of Biomedical Signals
- 1.2.1. The action potential
- 1.2.2. The electroneurogram (ENG)
- 1.2.3. The electromyogram (EMG)
- 1.2.4. The electrocardiogram (ECG)
- 1.2.5. The electroencephalogram (EEG)
- 1.2.6. Event-related potentials (ERPs)
- 1.2.7. The electrogastrogram (EGG)
- 1.2.8. The phonocardiogram (PCG)
- 1.2.9. The carotid pulse (CP)
- 1.2.10. Signals from catheter-tip sensors
- 1.2.11. The speech signal
- 1.2.12. The vibromyogram (VMG)
- 1.2.13. The vibroarthrogram (VAG)
- 1.2.14. Oto-acoustic emission signals
- 1.3. Objectives of Biomedical Signal Analysis
- 1.4. Difficulties in Biomedical Signal Analysis
- 1.5. Computer-aided Diagnosis
- 1.6. Remarks
- 1.7. Study Questions and Problems
- 1.8. Laboratory Exercises and Projects
- 2. Concurrent, Coupled, and Correlated Processes
- 2.1. Problem Statement
- 2.2. Illustration of the Problem with Case-studies
- 2.2.1. The electrocardiogram and the phonocardiogram
- 2.2.2. The phonocardiogram and the carotid pulse
- 2.2.3. The ECG and the atrial electrogram
- 2.2.4. Cardio-respiratory interaction
- 2.2.5. The electromyogram and the vibromyogram
- 2.2.6. The knee-joint and muscle vibration signals
- 2.3. Application: Segmentation of the PCG
- 2.4. Remarks
- 2.5. Study Questions and Problems
- 2.6. Laboratory Exercises and Projects
- 3. Filtering for Removal of Artifacts
- 3.1. Problem Statement
- 3.1.1. Random noise, structured noise, and physiological interference
- 3.1.2. Stationary versus nonstationary processes
- 3.2. Illustration of the Problem with Case-studies
- 3.2.1. Noise in event-related potentials
- 3.2.2. High-frequency noise in the ECG
- 3.2.3. Motion artifact in the ECG
- 3.2.4. Power-line interference in ECG signals
- 3.2.5. Maternal interference in fetal ECG
- 3.2.6. Muscle-contraction interference in VAG signals
- 3.2.7. Potential solutions to the problem
- 3.3. Time-domain Filters
- 3.3.1. Synchronized averaging
- 3.3.2. Moving-average filters
- 3.3.3. Derivative-based operators to remove low-frequency artifacts
- 3.4. Frequency-domain Filters
- 3.4.1. Removal of high-frequency noise: Butterworth lowpass filters
- 3.4.2. Removal of low-frequency noise: Butterworth highpass filters
- 3.4.3. Removal of periodic artifacts: Notch and comb filters
- 3.5. Optimal Filtering: The Wiener Filter
- 3.6. Adaptive Filters for Removal of Interference
- 3.6.1. The adaptive noise canceler
- 3.6.2. The least-mean-squares adaptive filter
- 3.6.3. The recursive least-squares adaptive filter
- 3.7. Selecting an Appropriate Filter
- 3.8. Application: Removal of Artifacts in the ECG
- 3.9. Application: Maternal - Fetal ECG
- 3.10. Application: Muscle-contraction Interference
- 3.11. Remarks
- 3.12. Study Questions and Problems
- 3.13. Laboratory Exercises and Projects
- 4. Event Detection
- 4.1. Problem Statement
- 4.2. Illustration of the Problem with Case-studies
- 4.2.1. The P, QRS, and T waves in the ECG
- 4.2.2. The first and second heart sounds
- 4.2.3. The dicrotic notch in the carotid pulse
- 4.2.4. EEG rhythms, waves, and transients
- 4.3. Detection of Events and Waves
- 4.3.1. Derivative-based methods for QRS detection
- 4.3.2. The Pan-Tompkins algorithm for QRS detection
- 4.3.3. Detection of the dicrotic notch
- 4.4. Correlation Analysis of EEG channels
- 4.4.1. Detection of EEG rhythms
- 4.4.2. Template matching for EEG spike-and-wave detection
- 4.5. Cross-spectral Techniques
- 4.5.1. Coherence analysis of EEG channels
- 4.6. The Matched Filter
- 4.6.1. Detection of EEG spike-and-wave complexes
- 4.7. Detection of the P Wave
- 4.8. Homomorphic Filtering
- 4.8.1. Generalized linear filtering
- 4.8.2. Homomorphic deconvolution
- 4.8.3. Extraction of the vocal-tract response
- 4.9. Application: ECG Rhythm Analysis
- 4.10. Application: Identification of Heart Sounds
- 4.11. Application: Detection of the Aortic Component of S2
- 4.12. Remarks
- 4.13. Study Questions and Problems
- 4.14. Laboratory Exercises and Projects
- 5. Waveshape and Waveform Complexity
- 5.1. Problem Statement
- 5.2. Illustration of the Problem with Case-studies
- 5.2.1. The QRS complex in the case of bundle-branch block
- 5.2.2. The effect of myocardial ischemia and infarction on QRS waveshape
- 5.2.3. Ectopic beats
- 5.2.4. EMG interference pattern complexity
- 5.2.5. PCG intensity patterns
- 5.3. Analysis of Event-related Potentials
- 5.4. Morphological Analysis of ECG Waves
- 5.4.1. Correlation coefficient
- 5.4.2. The minimum-phase correspondent and signal length
- 5.4.3. ECG waveform analysis
- 5.5. Envelope Extraction and Analysis
- 5.5.1. Amplitude demodulation
- 5.5.2. Synchronized averaging of PCG envelopes
- 5.5.3. The envelogram
- 5.6. Analysis of Activity
- 5.6.1. The root mean-squared value
- 5.6.2. Zero-crossing rate
- 5.6.3. Turns count
- 5.6.4. Form factor
- 5.7. Application: Normal and Ectopic ECG Beats
- 5.8. Application: Analysis of Exercise ECG
- 5.9. Application: Analysis of Respiration
- 5.10. Application: Correlates of Muscular Contraction
- 5.11. Remarks
- 5.12. Study Questions and Problems
- 5.13. Laboratory Exercises and Projects
- 6. Frequency-domain Characterization
- 6.1. Problem Statement
- 6.2. Illustration of the Problem with Case-studies
- 6.2.1. The effect of myocardial elasticity on heart sound spectra
- 6.2.2. Frequency analysis of murmurs to diagnose valvular defects
- 6.3. The Fourier Spectrum
- 6.4. Estimation of the Power Spectral Density Function
- 6.4.1. The periodogram
- 6.4.2. The need for averaging
- 6.4.3. The use of windows: Spectral resolution and leakage
- 6.4.4. Estimation of the autocorrelation function
- 6.4.5. Synchronized averaging of PCG spectra
- 6.5. Measures Derived from PSDs
- 6.5.1. Moments of PSD functions
- 6.5.2. Spectral power ratios
- 6.6. Application: Evaluation of Prosthetic Valves
- 6.7. Remarks
- 6.8. Study Questions and Problems
- 6.9. Laboratory Exercises and Projects
- 7. Modeling Biomedical Systems
- 7.1. Problem Statement
- 7.2. Illustration of the Problem
- 7.2.1. Motor-unit firing patterns
- 7.2.2. Cardiac rhythm
- 7.2.3. Formants and pitch in speech
- 7.2.4. Patello-femoral crepitus
- 7.3. Point Processes
- 7.4. Parametric System Modeling
- 7.5. Autoregressive or All-pole Modeling
- 7.5.1. Spectral matching and parameterization
- 7.5.2. Optimal model order
- 7.5.3. Relationship between AR and cepstral coefficients
- 7.6. Pole-zero Modeling
- 7.6.1. Sequential estimation of poles and zeros
- 7.6.2. Iterative system identification
- 7.6.3. Homomorphic prediction and modeling
- 7.7. Electromechanical Models of Signal Generation
- 7.7.1. Sound generation in coronary arteries
- 7.7.2. Sound generation in knee joints
- 7.8. Application: Heart-rate Variability
- 7.9. Application: Spectral Modeling and Analysis of PCG Signals
- 7.10. Application: Coronary Artery Disease
- 7.11. Remarks
- 7.12. Study Questions and Problems
- 7.13. Laboratory Exercises and Projects
- 8. Analysis of Nonstationary Signals
- 8.1. Problem Statement
- 8.2. Illustration of the Problem with Case-studies
- 8.2.1. Heart sounds and murmurs
- 8.2.2. EEG rhythms and waves
- 8.2.3. Articular cartilage damage and knee-joint vibrations
- 8.3. Time-variant Systems
- 8.3.1. Characterization of nonstationary signals and dynamic systems
- 8.4. Fixed Segmentation
- 8.4.1. The short-time Fourier transform
- 8.4.2. Considerations in short-time analysis
- 8.5. Adaptive Segmentation
- 8.5.1. Spectral error measure
- 8.5.2. ACF distance
- 8.5.3. The generalized likelihood ratio
- 8.5.4. Comparative analysis of the ACF, SEM, and GLR methods
- 8.6. Use of Adaptive Filters for Segmentation
- 8.6.1. Monitoring the RLS filter
- 8.6.2. The RLS lattice filter
- 8.7. Application: Adaptive Segmentation of EEG Signals
- 8.8. Application: Adaptive Segmentation of PCG Signals
- 8.9. Application: Time-varying Analysis of Heart-rate Variability
- 8.10. Remarks
- 8.11. Study Questions and Problems
- 8.12. Laboratory Exercises and Projects
- 9. Pattern Classification and Diagnostic Decision
- 9.1. Problem Statement
- 9.2. Illustration of the Problem with Case-studies
- 9.2.1. Diagnosis of bundle-branch block
- 9.2.2. Normal or ectopic ECG beat?
- 9.2.3. Is there an alpha rhythm?
- 9.2.4. Is a murmur present?
- 9.3. Pattern Classification
- 9.4. Supervised Pattern Classification
- 9.4.1. Discriminant and decision functions
- 9.4.2. Distance functions
- 9.4.3. The nearest-neighbor rule
- 9.5. Unsupervised Pattern Classification
- 9.5.1. Cluster-seeking methods
- 9.6. Probabilistic Models and Statistical Decision
- 9.6.1. Likelihood functions and statistical decision
- 9.6.2. Bayes classifier for normal patterns
- 9.7. Logistic Regression Analysis
- 9.8. The Training and Test Steps
- 9.8.1. The leave-one-out method
- 9.9. Neural Networks
- 9.10. Measures of Diagnostic Accuracy and Cost
- 9.10.1. Receiver operating characteristics
- 9.10.2. McNemar's test of symmetry
- 9.11. Reliability of Classifiers and Decisions
- 9.12. Application: Normal versus Ectopic ECG Beats
- 9.13. Application: Detection of Knee-joint Cartilage Pathology
- 9.14. Remarks
- 9.15. Study Questions and Problems
- 9.16. Laboratory Exercises and Projects
- References
- Index
