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Adaptive Predictors for Extracting Physiological Signals in Two

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Adaptive Predictors for Extracting Physiological Signals in Two ADAPTIVE PREDICTORS FOR EXTRACTING PHYSIOLOGICAL SIGNALS IN TWO MODERN BIOINSTRUMENTS by Brently W. Robinson APPROVED BY SUPERVISORY COMMITTEE: Dr. Mohammad Saquib Dr. Issa Panahi Dr. Naofal Al-Dhahir Dr. P.K. Rajasekaran Copyright 2019 Brently W. Robinson All rights reserved To All My Family. ADAPTIVE PREDICTORS FOR EXTRACTING PHYSIOLOGICAL SIGNALS IN TWO MODERN BIOINSTRUMENTS by BRENTLY W. ROBINSON, BS, MS DISSERTATION Presented to the Faculty of The University of Texas at Dallas in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY IN ELECTRICAL ENGINEERING THE UNIVERSITY OF TEXAS AT DALLAS August 2019 ACKNOWLEDGMENTS I would like to express deep gratitude and appreciation to Dr. Mohammad Saquib, my PhD advisor, who consistently and patiently encouraged me through periods of personal challenges and academic development. Apart from his support and guidance this research would not have come to fruition. I would also like to acknowledge and thank, my supervisory committee members, Dr. Issa Panahi, Dr. Naofal Al-Dhahir, and Dr. P.K. Rajasekaran. They provided feedback and direction through their comments and suggestions that helped to develop my research into a more comprehensive work. Finally, I would like to acknowledge and remember Dr. Phillip Loizou, an original supervisory committee member, whose guidance brought me to UTD and who was always wiling to share both personal and academic advice. My research was, in part, funded by the Raytheon Educational Assistance and Advanced Studies Program. July 2019 v ADAPTIVE PREDICTORS FOR EXTRACTING PHYSIOLOGICAL SIGNALS IN TWO MODERN BIOINSTRUMENTS Brently W. Robinson, PhD The University of Texas at Dallas, 2019 Supervising Professor: Dr. Mohammad Saquib Physiological signals are at the core of understanding, diagnosing, and treating the human body. Those provide valuable insight into the internal function and state of systems within the anatomy. Depending on the system being observed, a physiological signal can either be a source of information or a source of interference. This dissertation first examines physiological hand tremor, the body's response to stress, tiredness, or hunger, as a source of interference during microsurgery. It then examines the electrocardiogram (ECG), a source of vital information about the condition of the heart, when corrupted by broadband interference. Examination of these two physiological signals, obtained by bioinstruments, leads us to develop novel real-time adaptive predictors. Based on Kalman adaptation principle, an adaptive predictor is developed for removing physio- logical hand tremor and a scalable, cascaded predictor is designed for removing broadband interference from the ECG. Due to the real-time requirement of bioinstruments, this dissertation addresses the issues with implementing adaptive algorithms in fixed-point representation. A proposed modified binary floating-point format is presented and is shown to overcome the prior known issues associated with fixed-point implementations and demonstrated for removal of physiological hand tremor. vi TABLE OF CONTENTS ACKNOWLEDGMENTS . v ABSTRACT . vi LIST OF FIGURES . ix LIST OF TABLES . xi CHAPTER 1 INTRODUCTION . 1 1.1 Overview . 1 1.2 Background . 2 1.2.1 Cancelling Broadband Interference . 3 1.2.2 Cancelling Narrowband Interference . 6 1.3 Motivation . 7 1.4 Outline . 9 1.5 Contributions . 9 CHAPTER 2 ADAPTIVE PREDICTORS . 11 2.1 Introduction . 11 2.2 Wiener Filter . 11 2.3 Adaptive Filter . 14 2.3.1 Cost Function . 16 2.3.2 Minimization Algorithm . 17 2.4 Adaptive Predictor . 19 2.5 Conclusion . 25 CHAPTER 3 PREDICTING PHYSIOLOGICAL HAND TREMOR DURING MICRO- SURGERY . 26 3.1 Introduction . 26 3.2 System Model . 28 3.3 Proposed Algorithm . 29 3.4 Performance . 33 3.5 Hardware Implementation . 33 3.5.1 Fixed-Point Representation . 34 vii 3.5.2 Floating-Point Representation . 40 3.5.3 Comparison . 48 3.6 Conclusion . 50 CHAPTER 4 ENHANCEMENT OF THE ELECTROCARDIOGRAM USING ADAP- TIVE COHERENT AVERAGING . 51 4.1 Introduction . 51 4.2 Proposed Structure . 55 4.3 Cascaded Architecture . 58 4.4 Analysis . 61 4.4.1 Derivation of Optimum Filter at Stage 1 . 61 4.4.2 Derivation of Optimum Filter at Stage n . 62 4.4.3 Derivation of MSE at Stage n ...................... 64 4.5 Performance . 66 4.5.1 Least Mean Squares . 67 4.5.2 Recursive Least Squares . 67 4.5.3 Numerical Results . 68 4.5.4 Synthetic ECG Results . 69 4.6 Conclusion . 70 CHAPTER 5 CONCLUSIONS . 80 5.1 Summary . 80 5.2 Future Works . 81 REFERENCES . 82 BIOGRAPHICAL SKETCH . 87 CURRICULUM VITAE viii LIST OF FIGURES 1.1 The adaptive noise canceller [55]. 2 1.2 The implementation of adaptive cancellation for electrosurgical interference [57]. 3 1.3 The filter model for the cancellation of broadband interference [55]. 4 1.4 The time-sequenced adaptive filter [13]. 5 1.5 The adaptive recurrent filter [47]. 5 1.6 The filter model for the cancellation of narrowband interference [55]. 6 1.7 The Fourier linear combiner [51]. 7 1.8 The weighted-frequency Fourier linear combiner [35]. 8 2.1 The FIR transversal filter structure. 13 2.2 The Wiener filter structure. 13 2.3 The adaptive filter structure. 14 2.4 The adaptive predictor structure. 20 2.5 The adaptive predictor for cancelling broadband interference. 22 2.6 The adaptive predictor for cancelling narrowband interference. 23 2.7 The adaptive predictor with signal leakage. 24 3.1 The hand tremor measurement and cancellation system model. 28 3.2 The adaptive predictor used for hand tremor cancellation. 29 3.3 Example of explosive divergence and the stalling effect. 35 3.4 The mean square error of 64 bit fixed-point RLS. 37 3.5 The mean square error of 64 bit fixed-point Kalman. 38 3.6 The mean square error of 32 bit fixed-point RLS. 38 3.7 The mean square error of 32 bit fixed-point Kalman. 39 3.8 The mean square error of 16 bit fixed-point RLS. 39 3.9 The mean square error of 16 bit fixed-point Kalman. 40 3.10 The 16 bit half precision binary floating-point format. 41 3.11 The binary floating-point unpack process. 41 3.12 The mean square error of 64 bit floating-point RLS. 44 3.13 The mean square error of 64 bit floating-point Kalman. 44 ix 3.14 The mean square error of 32 bit floating-point RLS. 45 3.15 The mean square error of 32 bit floating-point Kalman. 45 3.16 The mean square error of 16 bit floating-point RLS. 46 3.17 The mean square error of 16 bit floating-point Kalman. 46 3.18 The 24 bit extended mantissa half precision binary floating-point format. 47 3.19 The mean square error of 24 bit floating-point RLS. 47 3.20 The mean square error of 24 bit fixed-point Kalman. 48 4.1 The synthetic ECG signal model. 52 4.2 The frequency spectrum of the synthetic ECG signal. 53 4.3 The reoccurence interval of the synthetic ECG signal model. 54 4.4 The adaptive predictor structure. 55 4.5 The adaptive coherent averaging structure. 56 4.6 The cascade form of the ACA structure. 58 4.7 The mean square error for the ACA Stage 1 (SNR = −5dB). 72 4.8 The mean square error for the ACA Stage 2 (SNR = −5dB). 72 4.9 The mean square error for the ACA Stage 3 (SNR = −5dB). 73 4.10 The mean square error for the ACA Stage 4 (SNR = −5dB). 73 4.11 The mean square error for the ACA Stage 5 (SNR = −5dB). 74 4.12 The mean square error for the ACA Stage 6 (SNR = −5dB). 74 4.13 The mean square error for the ACA Stage 7 (SNR = −5dB). 75 4.14 The mean square error for the ACA Stage 8 (SNR = −5dB). 75 4.15 The mean square error for the ACA Stage 1 (SNR = 5dB). 76 4.16 The mean square error for the ACA Stage 2 (SNR = 5dB). 76 4.17 The mean square error for the ACA Stage 3 (SNR = 5dB). 77 4.18 The mean square error for the ACA Stage 4 (SNR = 5dB). 77 4.19 The mean square error for the ACA Stage 5 (SNR = 5dB). 78 4.20 The mean square error for the ACA Stage.
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