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" Adaptive Predictors for Extracting Physiological Signals in Two Modern Bioinstruments "


Document Type : Latin Dissertation
Language of Document : English
Record Number : 1052864
Doc. No : TL51981
Main Entry : Robinson, Brently W.
Title & Author : Adaptive Predictors for Extracting Physiological Signals in Two Modern Bioinstruments\ Robinson, Brently W.Saquib, Mohammad
College : The University of Texas at Dallas
Date : 2019
Degree : Ph.D.
student score : 2019
Note : 101 p.
Abstract : 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 physiological 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.
Descriptor : Biomedical engineering
: Electrical engineering
Added Entry : Saquib, Mohammad
Added Entry : The University of Texas at Dallas
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