Adaptive Predictors for Extracting Physiological Signals in Two Modern Bioinstruments

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.