Increasing Signal to Noise Ratio and Minimising Artefacts in Biomedical Instrumentation Systems

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

Graphene textile smart clothing for wearable cardiac monitoring

Wearable electronics is a rapidly growing field that recently started to introduce successful commercial products into the consumer electronics market. Employment of biopotential signals in wearable systems as either biofeedbacks or control commands are expected to revolutionize many technologies including point of care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMIs), and brain–computer interfaces (BCIs). Since electrodes are regarded as a decisive part of such products, they have been studied for almost a decade now, resulting in the emergence of textile electrodes. This study reports on the synthesis and application of graphene nanotextiles for the development of wearable electrocardiography (ECG) sensors for personalized health monitoring applications. In this study, we show for the first time that the electrocardiogram was successfully obtained with graphene textiles placed on a single arm. The use of only one elastic armband, and an “all-textile-approach” facilitates seamless heart monitoring with maximum comfort to the wearer. The functionality of graphene textiles produced using dip coating and stencil printing techniques has been demonstrated by the non-invasive measurement of ECG signals, up to 98% excellent correlation with conventional pre-gelled, wet, silver/silver-chloride (Ag / AgCl) electrodes. Heart rate have been successfully determined with ECG signals obtained in different situations. The system-level integration and holistic design approach presented here will be effective for developing the latest technology in wearable heart monitoring devices