Increasing Signal to Noise Ratio and Minimising Artefacts in Biomedical Instrumentation Systems
type: CreativeWork
description: The research work described in this thesis was concerned with finding a novel method of minimising motion artefacts in biomedical instrumentation systems. The proposed solution, an Analog Frontend (AFE), was designed to detect any vertical (Y-Plane) or horizontal (X-Plane) movement of the electrode using two strain gauges, which were separated by 90° and fitted onto the electrode. The detected motion was fed back to the system for the removal of any motion artefact. The research started by emphasising the importance of minimising motion artefacts from biomedical signals and explaining how important it is for a clinical misinterpretation of the results. Hence, various motion artefact minimisation techniques undertaken by other researchers in the field were reviewed. This study covered different sources of artefacts, including the 40kHz powerline interference (PLI), 50/60kHz common-mode noise, white noise, and motion artefacts. The system was fully developed and tested and was firstly simulated using MATLAB Simulink tools to prove the effectiveness of the system before starting the implementation and build phase in the lab. The AFE system successfully produced a clean output signal, achieving an average correlation coefficient of 0.995. Also, the system output had a 98% SNR similarity with the clean source signal. Further, the system was then built and tested in the lab and successfully minimised the motion artefacts, achieving an average correlation coefficient of 0.974. Additionally, the final output had a 97.8% SNR similarity with the clean source signal. A novel test rig was developed to test the system with strain gauges. The system was able to remove the detected signal from the test rig and had an average correlation coefficient of 0.957. Lastly, the final output had a 94.2% SNR similarity with the clean source signal.
abstract: The research work described in this thesis was concerned with finding a novel method of minimising motion artefacts in biomedical instrumentation systems. The proposed solution, an Analog Frontend (AFE), was designed to detect any vertical (Y-Plane) or horizontal (X-Plane) movement of the electrode using two strain gauges, which were separated by 90° and fitted onto the electrode. The detected motion was fed back to the system for the removal of any motion artefact. The research started by emphasising the importance of minimising motion artefacts from biomedical signals and explaining how important it is for a clinical misinterpretation of the results. Hence, various motion artefact minimisation techniques undertaken by other researchers in the field were reviewed. This study covered different sources of artefacts, including the 40kHz powerline interference (PLI), 50/60kHz common-mode noise, white noise, and motion artefacts. The system was fully developed and tested and was firstly simulated using MATLAB Simulink tools to prove the effectiveness of the system before starting the implementation and build phase in the lab. The AFE system successfully produced a clean output signal, achieving an average correlation coefficient of 0.995. Also, the system output had a 98% SNR similarity with the clean source signal. Further, the system was then built and tested in the lab and successfully minimised the motion artefacts, achieving an average correlation coefficient of 0.974. Additionally, the final output had a 97.8% SNR similarity with the clean source signal. A novel test rig was developed to test the system with strain gauges. The system was able to remove the detected signal from the test rig and had an average correlation coefficient of 0.957. Lastly, the final output had a 94.2% SNR similarity with the clean source signal.
author: Zourob, Saddam S.
language: en
date published: 2020
date modified: 2026-07-24T03:43:41Z
provider: Oxford Brookes University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Oxford Brookes University

PropertyValue
property ID: DOI
value: 10.24384/nyx2-fr93