System theory and practical applications of biomedical signals

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.

The chapters are intentionally organized in groups of two chapters, with the first chapter describing a system theory technology, and the second chapter describing an industrial application of this technology. Each theory chapter contains a general overview of a system theory technology, which is intended as background material for the application chapter. Each application chapter contains a history of a highlighted medical instrument, summary of appropriate physiology, discussion of the problem of interest and previous empirical solutions, and review of a solution that utilizes the theory in the previous chapter.

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.

An Instructor Support FTP site is available from the Wiley editorial department: ftp://ftp.ieee.org/uploads/press/baura

Author Notes

Gail D. Baura is currently Director of Research and Development at CardioDynamics. Her research interests are the application of system theory to patient monitoring and insulin metabolism

Preface	p. xv
Nomenclature	p. xix
I Filters
1 System Theory and Frequency-Selective Filters	p. 3
1.1 Input-Output Description	p. 3
1.2 Linear Constant Coefficient Difference Equations	p. 6
1.3 Basic Frequency-Selective Filter Concepts	p. 9
1.4 Design of IIR Digital Filters from Analog Filters	p. 13
1.5 Design of FIR Filters by Windowing	p. 20
1.6 Pseudorandom Binary Sequence Filter	p. 24
1.7 Summary	p. 26
1.8 References	p. 27
1.9 Recommended Exercises	p. 28
2 Low Flow Rate Occlusion Detection Using Resistance Monitoring	p. 29
2.1 Physiology of Intravenous Drug Administration	p. 29
2.2 Intravenous Infusion Devices	p. 32
2.3 Problem Significance	p. 36
2.4 Resistance Monitoring in the IVAC Signature Edition Pump	p. 38
2.5 Summary	p. 42
2.6 References	p. 42
2.7 Matlab Exercises	p. 43
2.8 Intraarterial Blood Pressure Exercises	p. 43
3 Adaptive Filters	p. 46
3.1 Adaptive Noise Cancellation Proof	p. 46
3.2 Optimization Concepts	p. 48
3.3 Least Mean Squares Algorithm for Finite Impulse Response Filters	p. 49
3.4 Infinite Impulse Response Filters	p. 52
3.5 Adaptive Noise Cancellation	p. 60
3.6 Summary	p. 63
3.7 References	p. 65
3.8 Recommended Exercises	p. 65
4 Improved Pulse Oximetry	p. 66
4.1 Physiology of Oxygen Transport	p. 66
4.2 In Vitro Oxygen Measurements	p. 71
4.3 Problem Significance	p. 75
4.4 Adaptive Noise Cancellation in Masimo Software	p. 78
4.5 Summary	p. 84
4.6 References	p. 84
4.7 Noninvasive Blood Pressure Exercises	p. 85
5 Time-Frequency and Time-Scale Analysis	p. 87
5.1 Time-Frequency Representations	p. 91
5.2 Spectrogram	p. 94
5.3 Wigner Distribution	p. 97
5.4 Kernel Method	p. 100
5.5 Time-Scale Representations	p. 104
5.6 Scalograms	p. 105
5.7 Summary	p. 109
5.8 References	p. 109
5.9 Recommended Exercises	p. 111
6 Improved Impedance Cardiography	p. 112
6.1 Physiology of Cardiac Output	p. 112
6.2 In Vivo and In Vitro Cardiac Output Measurements	p. 116
6.3 Problem Significance	p. 122
6.4 Spectrogram Processing in Drexel Patents	p. 124
6.5 Wavelet Processing in CardioDynamics Software	p. 127
6.6 Summary	p. 133
6.7 References	p. 134
6.8 Electrocardiogram QRS Detection Exercises	p. 136
II Models for Real Time Processing
7 Linear System Identification	p. 141
7.1 The ARMAX Model and Variations	p. 141
7.2 Uniqueness Properties	p. 144
7.3 Model Identifiability	p. 145
7.4 Prediction Error Methods	p. 145
7.5 Instrumental Variable Methods	p. 150
7.6 Recursive Least Squares Algorithm	p. 152
7.7 Model Validation	p. 157
7.8 Summary	p. 159
7.9 References	p. 161
7.10 Recommended Exercises	p. 162
8 External Defibrillation Waveform Optimization	p. 163
8.1 Physiology	p. 163
8.2 External Defibrillation Waveforms	p. 167
8.3 Problem Significance	p. 171
8.4 Previous Studies	p. 173
8.5 Application of the ARX Model to Prediction of Transthoracic Impedance	p. 174
8.6 Transthoracic Impedance as the Basis of External Defibrillation Waveform Optimization	p. 185
8.7 Summary	p. 188
8.8 References	p. 189
8.9 Digital Thermometry Exercises	p. 192
9 Nonlinear System Identification	p. 195
9.1 Historical Review	p. 195
9.2 Supervised Multilayer Networks	p. 199
9.3 Unsupervised Neural Networks: Kohonen Network	p. 205
9.4 Unsupervised Networks: Adaptive Resonance Theory Network	p. 210
9.5 Model Validation	p. 212
9.6 Summary	p. 214
9.7 References	p. 215
9.8 Recommended Exercises	p. 217
10 Improved Screening for Cervical Cancer	p. 218
10.1 Physiology	p. 218
10.2 Pap Smear	p. 220
10.3 Problem Significance	p. 224
10.4 Semiautomation of Cervical Cancer Screening	p. 228
10.5 Cervical Cancer Screening Using Neural Networks	p. 231
10.6 Summary	p. 234
10.7 References	p. 235
10.8 Cardiac Output Exercises	p. 237
11 Fuzzy Models	p. 239
11.1 Historical Review	p. 239
11.2 Fuzzification	p. 242
11.3 Rule Base Inference	p. 244
11.4 Defuzzification	p. 246
11.5 Knowledge Base	p. 248
11.6 Model Validation	p. 251
11.7 Fuzzy Control	p. 251
11.8 Fuzzy Pattern Recognition	p. 255
11.9 Summary	p. 257
11.10 References	p. 260
11.11 Recommended Exercises	p. 261
12 Continuous Noninvasive Blood Pressure Monitoring: Proof of Concept	p. 262
12.1 Physiology	p. 262
12.2 In Vivo and In Vitro Blood Pressure Measurements	p. 264
12.3 Problem Significance	p. 266
12.4 Previous Studies	p. 267
12.5 Work Based on Digital Signal Processing	p. 273
12.6 Continuous Blood Pressure Measurement	p. 282
12.7 Summary	p. 284
12.8 References	p. 287
12.9 Infusion Pump Occlusion Alarm Exercises	p. 289
III Compartmental Models
13 The Linear Compartmental Model	p. 295
13.1 Protein Structure	p. 295
13.2 Experimental Design	p. 297
13.3 Kinetic Models	p. 299
13.4 Model Identifiability	p. 304
13.5 Nonlinear Least Squares Estimation	p. 306
13.6 Sampling Schedules	p. 308
13.7 Model Validation	p. 312
13.8 Summary	p. 313
13.9 References	p. 314
13.10 Recommended Exercises	p. 315
14 Pharmacologic Stress Testing Using Closed-Loop Drug Delivery	p. 316
14.1 Pharmacokinetics and Pharmacodynamics	p. 316
14.2 Control Theory	p. 320
14.3 Problem Significance	p. 325
14.4 Closed-Loop Drug Infusion in Pharmacological Stress Tests	p. 328
14.5 Summary	p. 334
14.6 References	p. 335
14.7 Peripheral Insulin Kinetics Exercises	p. 337
15 The Nonlinear Compartmental Model	p. 340
15.1 Michaelis-Menten Dynamics	p. 340
15.2 Bilinear Relation	p. 347
15.3 Summary	p. 351
15.4 Recommended References	p. 353
15.5 Recommended Exercises	p. 353
16 The Role of Nonlinear Compartmental Models in Development of Antiobesity Drugs	p. 354
16.1 Body Weight Regulation	p. 354
16.2 Receptor-Mediated Transport Across The Blood-Brain Barrier	p. 357
16.3 Problem Significance	p. 361
16.4 Previous Blood-Brain Barrier Insulin Studies	p. 363
16.5 Saturable Transport of Insulin from Plasma into the CNS	p. 368
16.6 Summary	p. 373
16.7 References	p. 375
16.8 Central Insulin Kinetics Exercises	p. 377
IV System Theory Implementation
17 Algorithm Implementation	p. 383
17.1 Data Types	p. 383
17.2 Digital Signal Processors	p. 385
17.3 Embedded Systems	p. 387
17.4 FDA Review of Medical Device Software	p. 389
17.5 Summary	p. 393
17.6 References	p. 394
18 The Need for More System Theory in Low-Cost Medical Applications	p. 395
18.1 Future Employment for Biomedical Engineering Graduate Students	p. 395
18.2 The Loss of Innovation in the Medical Device Industry	p. 396
18.3 Low-Cost Medical Monitoring and System Theory	p. 398
18.4 Addressing the Need for Innovation in a Cost-Conscious Environment	p. 401
18.5 References	p. 403
Glossary	p. 407
Index	p. 431

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