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Emerging Imaging

Monitoring blood glucose levels with imaging photoplethysmography

Project ID: 2019_066

1st supervisor: Jordi Alastruey, King’s College London
2nd supervisor: Pablo Lamata, King’s College London
Additional supervisors: John Dennis and Masih Nilchian (mypulses)

The global prevalence of diabetes will rise from 171 million in 2000 to 366 million in 2030 for all groups [1]. 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 [2,3]. 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 [4-6]. 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 [9,10]. 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 [11,12,13]. This is possible because the PPG wave is influenced by the cardiovascular, respiratory and autonomic nervous systems [14], which are all affected by the above conditions and physiological processes. Glucose level also affects these systems and, hence, PPG wave patterns [6,15].

The PPG wave can be easily acquired using healthcare devices but also consumer devices, including smartphones, tablets, and fitness devices [16]. 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.

References
[1] S. Wild, G. Roglic, A. Green, R. Sicree, and H. King. Global Prevalence of Diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care 27(5):1047–53, 2004.
[2] M. J. Fowler. Microvascular and macrovascular complications of diabetes. Clinical Diabetes 26(2):6, 2008.
[3] M. Nentwich. Diabetic retinopathy – Ocular complications of diabetes mellitus. WJD 6(3):489, 2015.
[4] A. Jina et al. Design, development, and evaluation of a novel microneedle array-based continuous glucose monitor. J Diabetes Sci Technol 8(3):483–7, 2014.
[5] J. Y. Lucisano, T. L. Routh, J. T. Lin, and D. A. Gough. Glucose monitoring in individuals with diabetes using a long-term implanted sensor/telemetry system and model. IEEE Trans Biomed Eng 64(9):1982–93, 2017.
[6] W. Villena Gonzales, A. Mobashsher, and A. Abbosh. 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.
[7] M. J. Rice, A. D. Pitkin, and D. B. Coursin. Glucose measurement in the operating room: More complicated than it seems. Anesthesia & Analgesia 110:1056–65, 2010.
[8] D. H. Wasserman. Four grams of glucose. American Journal of Physiology-Endocrinology and Metabolism 296(1):E11–E21, 2009.
[9] Millasseau SC, Guigui FG, Kelly RP, Prasad K, Cockcroft JR, Ritter JM, Chowienczyk PJ. Noninvasive assessment of the digital volume pulse. Comparison with the peripheral pressure pulse. Hypertension 36:952–6, 2000.
[10] Millasseau S, Ritter J, Takazawa K, Chowienczyk P. Contour analysis of the photoplethysmographic pulse measured at the finger. J Hypertens 24:1449–56, 2006.
[11] N. Bourdillon, S. Yazdani, M. Nilchian, A. Mariano, J.-M. Vesin, and G. P. Millet, “Overload blunts baroreflex only in overreached athletes,” Journal of Science and Medicine in Sport, vol. 21, no. 9, pp. 941–949, Sep. 2018.
[12] N. Bourdillon, M. Nilchian, and G. P. Millet, “Photoplethysmography Detection of Overreaching,” Medicine & Science in Sports & Exercise, vol. 51, no. 4, pp. 701–707, Apr. 2019.
[13] Charlton P, Celka P, Farukh B, Chowienczyk P, Alastruey J. Assessing mental stress from the photoplethysmogram: a numerical study. Physiol Measur 39(5):054001, 2018.
[14] Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Measur 28(3):R1–R39, 2007.
[15] V. P. Rachim and W.-Y. Chung. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sensors and Actuators B: Chemical 286:173–80, 2019.
[16] Charlton P, …, Alastruey J, et al. Breathing rate estimation from the electrocardiogram and photoplethysmogram: a review. IEEE Reviews in Biomedical Engineering 11:2-2018.

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