Biomedical Signal Analysis: A Case-Study Approach
Biomedical Signal Analysis: A Case-Study Approach
Jun 2012, Wiley-IEEE Press
DescriptionThe 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
About the Author.
Symbols and Abbreviations.
1 Introduction to Biomedical Signals.
1.1 The Nature of Biomedical Signals.
1.2 Examples of Biomedical Signals.
1.3 Objectives of Biomedical Signal Analysis.
1.4 Difficulties in Biomedical Signal Analysis.
1.5 Computer-aided Diagnosis.
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.
2.3 Application: Segmentation of the PCG.
2.5 Study Questions and Problems.
2.6 Laboratory Exercises and Projects.
3 Filtering for Removal of Artifacts.
3.1 Problem Statement.
3.2 Illustration of the Problem with Case-studies.
3.3 Time-domain Filters.
3.4 Frequency-domain Filters.
3.5 Optimal Filtering: The Wiener Filter.
3.6 Adaptive Filters for Removal of Interference.
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.
3.12 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.
4.3 Detection of Events and Waves.
4.4 Correlation Analysis of EEG channels.
4.5 Cross-spectral Techniques.
4.6 The Matched Filter.
4.7 Detection of the P Wave.
4.8 Homomorphic Filtering.
4.9 Application: ECG Rhythm Analysis.
4.10 Application: Identification of Heart Sounds.
4.11 Application: Detection of the Aortic Component of S2.
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.
5.3 Analysis of Event-related Potentials.
5.4 Morphological Analysis of ECG Waves.
5.5 Envelope Extraction and Analysis.
5.6 Analysis of Activity.
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.
5.12 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.
6.3 The Fourier Spectrum.
6.4 Estimation of the Power Spectral Density Function.
6.5 Measures Derived from PSDs.
6.6 Application: Evaluation of Prosthetic Valves.
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.
7.3 Point Processes.
7.4 Parametric System Modeling.
7.5 Autoregressive or All-pole Modeling.
7.6 Pole-zero Modeling.
7.7 Electromechanical Models of Signal Generation.
7.8 Application: Heart-rate Variability.
7.9 Application: Spectral Modeling and Analysis of PCG Signals.
7.10 Application: Coronary Artery Disease.
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.
8.3 Time-variant Systems.
8.4 Fixed Segmentation.
8.5 Adaptive Segmentation.
8.6 Use of Adaptive Filters for Segmentation.
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.
8.11 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.
9.3 Pattern Classification.
9.4 Supervised Pattern Classification.
9.5 Unsupervised Pattern Classification.
9.6 Probabilistic Models and Statistical Decision.
9.7 Logistic Regression Analysis.
9.8 The Training and Test Steps.
9.9 Neural Networks.
9.10 Measures of Diagnostic Accuracy and Cost.
9.11 Reliability of Classifiers and Decisions.
9.12 Application: Normal versus Ectopic ECG Beats.
9.13 Application: Detection of Knee-joint Cartilage Pathology.
9.15 Study Questions and Problems.
9.16 Laboratory Exercises and Projects.
"This book takes a problem-solving approach to biomedical signal analysis." (IEEE Signal Processing Magazine, Vol. 19, No. 4, July 2002)