Dr J Alastruey
Dr P Lamata
Dr J Dennis
Dr M Nilchian
No more applications being accepted
Competition Funded PhD Project (UK Students Only)
About the Project
This project is part of the joint King’s College London - Imperial College London EPSRC Centre for Doctoral Training (CDT) in Smart Medical Imaging: https://www.findaphd.com/phds/program/epsrc-centre-for-doctoral-training-cdt-in-smart-medical-imaging/?i132p2301
The global prevalence of diabetes will rise from 171 million in 2000 to 366 million in 2030 for all groups. Fluctuations of the glucose level (GL) may lead to microvascular and macrovascular complications and result in long-term disease including kidney problems, ocular disease, and heart failure. Therefore, regular and continuous monitoring of GL is highly important to avoid these problems in healthy subjects and help diabetic patients control/manage GL using medicines. Traditional GL monitoring currently involves pricking of the finger to draw blood and inserting a drop sample into a home-care device to determine GL. This is invasive, painful, time-consuming, costly, discontinuous, and poses risks of infection and tissue damage.
Minimally invasive systems using an implantable sensor to measure continuously GL are an alternative to finger prick. Examples include the commercialised systems Dexon G5 and freestyle libre. Unfortunately, the implantable sensor itself has a limited time span and suffers from delay and stability problems. Moreover, these devices still have some level of invasiveness. There have been several efforts to develop non-invasive biosensors to measure GL, but these are far from perfection since it is very challenging to directly measure glucose since this is colourless, present in all tissue in varying amounts inside and outside cells, in small amounts (100 millimetres of blood normally hold 0.1 grams of glucose only), and its chemical compound is very similar to many other compounds that are present throughout the body.
Rather than measuring actual levels of the glucose compounds, here we propose to image the pulse wave by photoplethysmography (PPG) and examine how this reacts to changes in GL. The amplitude of the pulse wave is determined by local blood perfusion, but its contour is determined by characteristics of the cardiovascular, respiratory, and autonomic nervous systems. Several in vivo studies have shown statistically significant correlations between variations in indices measured from the PPG wave pattern and several conditions such as physical overreaching, mental stress, and mental fatigue, as well as different sleep stages and the ability to discriminate between physical, mental and sleep deprived sources of fatigue. This is possible because the PPG wave is influenced by the cardiovascular, respiratory and autonomic nervous systems, which are all affected by the above conditions and physiological processes. Glucose level also affects these systems and, hence, PPG wave patterns.
The PPG wave can be easily acquired using healthcare devices but also consumer devices, including smartphones, tablets, and fitness devices. If it was possible to monitor GL from the PPG acquired from consumer devices, then it would have great utility for daily monitoring of sugar levels in diabetic and healthy subjects. In this project we will carry out in vivo and computational investigations to investigate the feasibility of our novel approach.
This is a project for a Home student for October 2020 start co-sponsored by Mypulses.com. Suitable qualifications are:
-Engineering (civil, telecom);
Deadline: 17 July 2020 but this project might be filled earlier if a suitable candidate is found. Please submit your application at your earliest opportunity.
 Villena Gonzales et al. The progress of glucose monitoring — A review of invasive to minimally and non-invasive techniques, devices and sensors. Sensors 19(4):800 (45 pp), 2019.
 Charlton et al. Assessing mental stress from the photoplethysmogram: a numerical study. Physiol Measur 39(5):054001, 2018.
 Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Measur 28(3):R1–R39, 2007.
 Charlton et al. Breathing rate estimation from the electrocardiogram and photoplethysmogram: a review. IEEE Reviews in Biomedical Engineering 11:2-2018.