Continuous glucose monitoring sensors: Past, present and future algorithmic challenges

Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems. Furthermore, CGM devices have stimulated the development of applications that were impossible to create without a continuous-time glucose signal, e.g., real-time predictive alerts of hypo/hyperglycemic episodes based on the prediction of future glucose concentration, automatic basal insulin attenuation methods for hypoglycemia prevention, and the artificial pancreas. However, CGM sensors’ lack of accuracy and reliability limited their usability in the clinical practice, calling upon the academic community for the development of suitable signal processing methods to improve CGM performance. The aim of this paper is to review the past and present algorithmic challenges of CGM sensors, to show how they have been tackled by our research group, and to identify the possible future ones

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Improving the clinical value and utility of CGM systems: issues and recommendations : a joint statement of the European Association for the Study of Diabetes and the American Diabetes Association Diabetes Technology Working Group

The first systems for continuous glucose monitoring (CGM) became available over 15 years ago. Many then believed CGM would revolutionise the use of intensive insulin therapy in diabetes; however, progress towards that vision has been gradual. Although increasing, the proportion of individuals using CGM rather than conventional systems for self-monitoring of blood glucose on a daily basis is still low in most parts of the world. Barriers to uptake include cost, measurement reliability (particularly with earlier-generation systems), human factors issues, lack of a standardised format for displaying results and uncertainty on how best to use CGM data to make therapeutic decisions. This scientific statement makes recommendations for systemic improvements in clinical use and regulatory (pre- and postmarketing) handling of CGM devices. The aim is to improve safety and efficacy in order to support the advancement of the technology in achieving its potential to improve quality of life and health outcomes for more people with diabetes

Improving the clinical value and utility of CGM systems: issues and recommendations: a joint statement of the European Association for the Study of Diabetes and the American Diabetes Association Diabetes Technology Working Group

The first systems for continuous glucose monitoring (CGM) became available over 15 years ago. Many then believed CGM would revolutionize the use of intensive insulin therapy in diabetes; however, progress toward that vision has been gradual. Although increasing, the proportion of individuals using CGM rather than conventional systems for self-monitoring of blood glucose on a daily basis is still low in most parts of the world. Barriers to uptake include cost, measurement reliability (particularly with earlier-generation systems), human factors issues, lack of a standardized format for displaying results, and uncertainty on how best to use CGM data to make therapeutic decisions. This Scientific Statement makes recommendations for systemic improvements in clinical use and regulatory (pre- and postmarketing) handling of CGM devices. The aim is to improve safety and efficacy in order to support the advancement of the technology in achieving its potential to improve quality of life and health outcomes for more people with diabetes

Minimizing hypoglycemia while maintaining glycemic control in diabetes

In the accompanying Perspective, Cryer identifies a number of different areas where therapeutic interventions have the potential to reduce hypoglycemia without compromising glycemic control. Some approaches provide well defined clinical benefits, a few offer dramatic reductions in hypoglycemia but remain out of reach for most people while others, although promising have yet to be properly evaluated. (Table 1) In this Perspective, I examine the evidence which underpins these interventions. It is beyond the scope of this article to review the data for each potential intervention in detail but the reader is directed to the appropriate source where appropriate. The Perspective focuses on treatment of Type 1 diabetes as most of the potential specific therapies have been evaluated in this group although I have commented in relation to recent trials of intensive therapy in Type 2 diabetes

A deep learning approach to diabetic blood glucose prediction

We consider the question of 30-minute prediction of blood glucose levels measured by continuous glucose monitoring devices, using clinical data. While most studies of this nature deal with one patient at a time, we take a certain percentage of patients in the data set as training data, and test on the remainder of the patients; i.e., the machine need not re-calibrate on the new patients in the data set. We demonstrate how deep learning can outperform shallow networks in this example. One novelty is to demonstrate how a parsimonious deep representation can be constructed using domain knowledge

Comparison of glycemic excursion in patients with new onset type 2 diabetes mellitus before and after treatment with repaglinide

Due to industrialization and sedentary life, incidence of type 2 diabetes (DM2) is increasing seriously. Repaglinide is a glucose reducing agent that predominantly reduces post-prandial glucose. Continuous glucose monitoring system (CGMS) monitors blood glucose excursions over a 3-day period. CGMS can be used as a therapeutic and diagnostic instrument in diabetics. There are not enough studies about using CGMS in DM2. The aim of this study was to determine the blood glucose excursions in patients with new onset of DM2. 10 patients with new onset of DM2 were entered to this study. As the first therapeutic management, patients received diabetic diet and moderate exercise for 3-weeks, if they did not achieve blood glucose goal (Fasting blood glucoser (FBG) <120mg/dl, 2-hour postprandial blood glucose (2hpp) <180mg/dl), were considered to undergo 3-days CGMS at baseline and after 4-weeks on Repaglinide (0.5mg three times before meals). Mean excursions of blood glucose were not different at the onset and at the end of treatment (6±4.05 VS 7.6±5.2 episodes, P=0.49). There were also no significant differences between mean duration of hypoglycemic episodes (zero VS 5.1±14.1 hours, P =0.28) and hyperglycemic episodes before and after therapy (7.6±5.2 VS 5.7±4.1, P=0.42), but mean hyperglycemia duration was significantly reduced at the end of therapy (21±26.17 VS 57.7±35.3, P=0.001). Patients experienced a mean of 0.3±0.67 episodes of hypoglycemia after therapy showed no significant difference before it (P =0.19). Mean FBG (with CGMS) was significantly lower after therapy than before it (142.9±54.31 VS 222.9±82.6, P <0.001). This study showed the usefulness of CGMS not only as a diagnostic but also as an educational and therapeutic tool that in combination with Repaglinide (with the lowest effective dose and duration) can significantly reduce FBG and glycemic excursions in DM2 patients and hypoglycemic events are low. © Hezarkhani et al