Adaptive Predictors for Extracting Physiological Signals in Two Modern Bioinstruments
نام عام مواد
[Thesis]
نام نخستين پديدآور
Robinson, Brently W.
نام ساير پديدآوران
Saquib, Mohammad
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
The University of Texas at Dallas
تاریخ نشرو بخش و غیره
2019
يادداشت کلی
متن يادداشت
101 p.
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
Ph.D.
کسي که مدرک را اعطا کرده
The University of Texas at Dallas
امتياز متن
2019
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
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.
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Biomedical engineering
اصطلاح موضوعی
Electrical engineering
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )