System Theory And Practical Applications Of Biomedical Signals
暫譯: 生物醫學信號的系統理論與實務應用

Description:

A volume in the IEEE Press Series in Biomedical Engineering
Metkin Akay, Series Editor
Endorsed by the IEEE Engineering in Medicine and Biology Society

A valuable synthesis of system theory and real-world applications for biomedical instrumentation

System theory is becoming increasingly important to medical applications. Yet, biomedical and digital signal processing researchers rarely have expertise in practical medical applications, and medical instrumentation designers usually are unfamiliar with system theory. System Theory and Practical Applications for Biomedical Signals bridges those gaps in a practical manner, showing how various aspects of system theory are put into practice by industry.

Written from the perspective of an industry insider who actually made useful products based on the theory, this pragmatic guide combines traditional DSP and compartmental modeling, as well as pairing in-depth discussions of practical medical instrumentation applications and system theory.

Biomedical and DSP academic researchers pursuing grants and industry funding will find its real-world approach extremely valuable. Its in-depth discussion of the theoretical issues will clarify for medical instrumentation managers how system theory can compensate for less-than-ideal sensors. With application MATLAB exercises and suggestions for system theory course work included, the text also fills the need for detailed information for students or practicing engineers interested in instrument design.

 Table of Contents:

Preface.

Nomenclature.

I FILTERS.

1 System Theory and Frequency-Selective Filters.

1.1 Input-Output Description.

1.2 Linear Constant Coefficient Difference Equations.

1.3 Basic Frequency-Selective Filter Concepts.

1.4 Design of IIR Digital Filters from Analog Filters.

1.5 Design of FIR Filters by Windowing.

1.6 Pseudorandom Binary Sequence Filter.

1.7 Summary.

1.8 References.

1.9 Recommended Exercises.

2 Low Flow Rate Occlusion Detection Using Resistance Monitoring.

2.1 Physiology of Intravenous Drug Administration.

2.2 Intravenous Infusion Devices.

2.3 Problem Significance.

2.4 Resistance Monitoring in the IVAC Signature Edition Pump.

2.5 Summary.

2.6 References.

2.7 Matlab Exercises.

2.8 Intraarterial Blood Pressure Exercises.

3 Adaptive Filters.

3.1 Adaptive Noise Cancellation Proof.

3.2 Optimization Concepts.

3.3 Least Mean Squares Algorithm for Finite Impulse Response Filters.

3.4 Infinite Impulse Response Filters.

3.5 Adaptive Noise Cancellation.

3.6 Summary.

3.7 References.

3.8 Recommended Exercises.

4 Improved Pulse Oximetry.

4.1 Physiology of Oxygen Transport.

4.2 In Vitro Oxygen Measurements.

4.3 Problem Significance.

4.4 Adaptive Noise Cancellation in Masimo Software.

4.5 Summary.

4.6 References.

4.7 Noninvasive Blood Pressure Exercises.

5 Time-Frequency and Time-Scale Analysis.

5.1 Time-Frequency Representations.

5.2 Spectrogram.

5.3 Wigner Distribution.

5.4 Kernel Method.

5.5 Time-Scale Representations.

5.6 Scalograms.

5.7 Summary.

5.8 References.

5.9 Recommended Exercises.

6 Improved Impedance Cardiography.

6.1 Physiology of Cardiac Output.

6.2 In Vivo and In Vitro Cardiac Output Measurements.

6.3 Problem Significance.

6.4 Spectrogram Processing in Drexel Patents.

6.5 Wavelet Processing in CardioDynamics Software.

6.6 Summary.

6.7 References.

6.8 Electrocardiogram QRS Detection Exercises.

II MODELS FOR REAL TIME PROCESSING.

7 Linear System Identification.

7.1 The ARMAX Model and Variations.

7.2 Uniqueness Properties.

7.3 Model Identifiability.

7.4 Prediction Error Methods.

7.5 Instrumental Variable Methods.

7.6 Recursive Least Squares Algorithm.

7.7 Model Validation.

7.8 Summary.

7.9 References.

7.10 Recommended Exercises.

8 External Defibrillation Waveform Optimization.

8.1 Physiology.

8.2 External Defibrillation Waveforms.

8.3 Problem Significance.

8.4 Previous Studies.

8.5 Application of the ARX Model to Prediction of Transthoracic Impedance.

8.6 Transthoracic Impedance as the Basis of External Defibrillation Waveform Optimization.

8.7 Summary.

8.8 References.

8.9 Digital Thermometry Exercises.

9 Nonlinear System Identification.

9.1 Historical Review.

9.2 Supervised Multilayer Networks.

9.3 Unsupervised Neural Networks: Kohonen Network.

9.4 Unsupervised Networks: Adaptive Resonance Theory Network.

9.5 Model Validation.

9.6 Summary.

9.7 References.

9.8 Recommended Exercises.

10 Improved Screening for Cervical Cancer.

10.1 Physiology.

10.2 Pap Smear.

10.3 Problem Significance.

10.4 Semiautomation of Cervical Cancer Screening.

10.5 Cervical Cancer Screening Using Neural Networks.

10.6 Summary.

10.7 References.

10.8 Cardiac Output Exercises.

11 Fuzzy Models.

11.1 Historical Review.

11.2 Fuzzification.

11.3 Rule Base Inference.

11.4 Defuzzification.

11.5 Knowledge Base.

11.6 Model Validation.

11.7 Fuzzy Control.

11.8 Fuzzy Pattern Recognition.

11.9 Summary.

11.10 References.

11.11 Recommended Exercises.

12 Continuous Noninvasive Blood Pressure Monitoring: Proof of Concept.

12.1 Physiology.

12.2 In Vivo and In Vitro Blood Pressure Measurements.

12.3 Problem Significance.

12.4 Previous Studies.

12.5 Work Based on Digital Signal Processing.

12.6 Continuous Blood Pressure Measurement.

12.7 Summary.

12.8 References.

12.9 Infusion Pump Occlusion Alarm Exercises.

III COMPARTMENTAL MODELS.

13 The Linear Compartmental Model.

13.1 Protein Structure.

13.2 Experimental Design.

13.3 Kinetic Models.

13.4 Model Identifiability.

13.5 Nonlinear Least Squares Estimation.

13.6 Sampling Schedules.

13.7 Model Validation.

13.8 Summary.

13.9 References.

13.10 Recommended Exercises.

14 Pharmacologic Stress Testing Using Closed-Loop Drug Delivery.

14.1 Pharmacokinetics and Pharmacodynamics.

14.2 Control Theory.

14.3 Problem Significance.

14.4 Closed-Loop Drug Infusion in Pharmacological Stress Tests.

14.5 Summary.

14.6 References.

14.7 Peripheral Insulin Kinetics Exercises.

15 The Nonlinear Compartmental Model.

15.1 Michaelis-Menten Dynamics.

15.2 Bilinear Relation.

15.3 Summary.

15.4 Recommended References.

15.5 Recommended Exercises.

16 The Role of Nonlinear Compartmental Models in Development of Antiobesity Drugs.

16.1 Body Weight Regulation.

16.2 Receptor-Mediated Transport Across The Blood-Brain Barrier.

16.3 Problem Significance.

16.4 Previous Blood-Brain Barrier Insulin Studies.

16.5 Saturable Transport of Insulin from Plasma into the CNS.

16.6 Summary.

16.7 References.

16.8 Central Insulin Kinetics Exercises.

IV SYSTEM THEORY IMPLEMENTATION.

17 Algorithm Implementation.

17.1 Data Types.

17.2 Digital Signal Processors.

17.3 Embedded Systems.

17.4 FDA Review of Medical Device Software.

17.5 Summary.

17.6 References.

18 The Need for More System Theory in Low-Cost Medical Applications.

18.1 Future Employment for Biomedical Engineering Graduate Students.

18.2 The Loss of Innovation in the Medical Device Industry.

18.3 Low-Cost Medical Monitoring and System Theory.

18.4 Addressing the Need for Innovation in a Cost-Conscious Environment.

18.5 References.

Glossary.

Index.