Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments

Abstract

The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin-treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under-dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in-development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near-infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle-type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management.

Keywords: algorithms; devices; diabetes mellitus; hypoglycaemia; sensors.

FIGURE 1

Mapping of hypoglycaemia detection and prediction techniques

FIGURE 2

PRISMA flow diagram

FIGURE 3

Process from glucose sensing to hypoglycaemic event alert found in many available devices

FIGURE 4

Multivariable blood glucose prediction. In addition to glucose value, others inputs are added to increase accuracy. A blood glucose prediction value is calculated, and classification rules allow evaluating the incoming hypoglycaemia event

FIGURE 5

Multivariable event prediction. In this other model, the algorithm is trained directly for hypoglycaemia event prediction

FIGURE 6

Illustration of interindividual differences in heartbeats during hypoglycaemia events. The solidlines represent the mean of all the heartbeats that correspond to each subject in the training dataset: green during normal glucose levels, red during hypoglycaemic events. The comparison among four different subjects highlighted the fact that each subject may have a different ECG waveform during hypoglycaemic events. For instance, Subjects 1 and 2 present a visibly longer QT interval during hypoglycaemic events, differently from Subjects 3 and 4. Reproduced from Porumb et al.

FIGURE 7

Encephalogram (EEG) segments during euglycaemia and hypoglycaemia. Each segment represents a 5‐s interval of EEG recordings during each phase, showing a higher amplitude in the low‐frequency bands and greater regularity during hypoglycaemia episodes