Bio-sensing and wearables in healthcare
This research theme focuses on the development and use of devices that can be attached to the human body to detect and monitor changes in the body’s vital parameters.
Non-invasive biosensing textiles
Dr Carol Crean and her team within the School of Chemistry and Chemical Engineering have developed fibres that can be sewn into bandages or clothing to measure skin pH for wound monitoring. They have also developed textiles which incorporate fibre electrodes to extract lithium – a drug used to treat bipolar disorder – from under the skin, and measure lithium drug levels, as well as textile systems to measure the stress hormone cortisol.
Novel optical micro-imaging to understand the onset of cardiovascular disease
Collaborative research by Professor Christian Heiss (School of Biosciences and Medicine) and other experts in medicine, biology, physics, biomedical engineering and artificial intelligence aims to better understand vascular ageing and the process of developing cardiovascular disease (CVD). CVD is one of the biggest age-related diseases causing disability and death in the UK and has been identified by the NHS as a clinical priority for the next 10 years.
The Surrey team is working with industrial partners to develop novel optical imaging methods which will accelerate understanding of early ageing of the microvasculature and how this leads to CVD. This research could potentially lead to earlier identification of CVD and the development of novel therapies.
Using AI to enable early detection of paroxsymal atrial fibrillation (PAF)
Paroxsymal atrial fibrillation (PAF) is the most common arrhythmia worldwide and poses a significant risk of morbidity and mortality in humans due to embolic stroke, congestive heart failure and acute coronary syndrome. In the equine world, PAF is one of the leading causes of sudden death in racehorses. The majority of human and equine patients have paroxysmal (intermittent) events, with the heart reverting back to a normal rhythm, which means these cases are typically not captured by routine electrocardiograms (ECG) checks.
Dr Kamalan Jeevaratnam of Surrey’s Vet School is working with clinicians and biophysical scientists to develop an algorithm for the accurate identification of individuals with PAF. Specifically, they seek to develop and validate a risk prediction algorithm for abnormal heart rhythm in humans and horses using computational analysis of ECG. This will aid early detection and intervention to prevent the disabling and life-threatening consequences of this disorder.