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

Optical based noninvasive glucose monitoring sensor prototype

Diabetes mellitus claims millions of lives every year. It affects the body in various ways by leading to many serious illnesses and premature mortality. Heart and kidney diseases, which are caused by diabetes, are increasing at an alarming rate. In this paper, we report a study of a noninvasive measurement technique to determine the glucose levels in the human body. Current existing methods to quantify the glucose level in the blood are predominantly invasive that involve taking the blood samples using finger pricking. In this paper, we report a spectroscopy-based noninvasive glucose monitoring system to measure glucose concentration. Near-infrared transmission spectroscopy is used and in vitro experiments are conducted, as well as in vivo. Our experimental study confirms a correlation between the sensor output voltage and glucose concentration levels. We report a low-cost prototype of spectroscopy-based noninvasive glucose monitoring system that demonstrates promising results in vitro and establishes a relationship between the optical signals and the changing levels of blood–glucose concentration

Diabetes Prediction Using Artificial Neural Network

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3

Expanded CURB-65: A new score system predicts severity of community-acquired pneumonia with superior efficiency

Aim of this study was to develop a new simpler and more effective severity score for communityacquired pneumonia (CAP) patients. A total of 1640 consecutive hospitalized CAP patients in Second Affiliated Hospital of Zhejiang University were included. The effectiveness of different pneumonia severity scores to predict mortality was compared, and the performance of the new score was validated on an external cohort of 1164 patients with pneumonia admitted to a teaching hospital in Italy. Using age≥ 65 years, LDH>230u/L, albumin<3.5g/dL, platelet count<100×109/L, confusion, urea>7mmol/L, respiratory rate≥30/min, low blood pressure, we assembled a new severity score named as expanded-CURB-65. The 30-day mortality and length of stay were increased along with increased risk score. The AUCs in the prediction of 30-day mortality in the main cohort were 0.826 (95%CI, 0.807–0.844), 0.801 (95%CI, 0.781–0.820), 0.756 (95%CI, 0.735–0.777), 0.793 (95%CI, 0.773–0.813) and 0.759 (95%CI, 0.737–0.779) for the expanded-CURB-65, PSI, CURB-65, SMART-COP and A-DROP, respectively. The performance of this bedside score was confirmed in CAP patients of the validation cohort although calibration was not successful in patients with health care-associated pneumonia (HCAP). The expanded CURB-65 is objective, simpler and more accurate scoring system for evaluation of CAP severity, and the predictive efficiency was better than other score systems

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

Oral application of L-menthol in the heat: From pleasure to performance

When menthol is applied to the oral cavity it presents with a familiar refreshing sensation and cooling mint flavour. This may be deemed hedonic in some individuals, but may cause irritation in others. This variation in response is likely dependent upon trigeminal sensitivity toward cold stimuli, suggesting a need for a menthol solution that can be easily personalised. Menthol’s characteristics can also be enhanced by matching colour to qualitative outcomes; a factor which can easily be manipulated by practitioners working in athletic or occupational settings to potentially enhance intervention efficacy. This presentation will outline the efficacy of oral menthol application for improving time trial performance to date, either via swilling or via co-ingestion with other cooling strategies, with an emphasis upon how menthol can be applied in ecologically valid scenarios. Situations in which performance is not expected to be enhanced will also be discussed. An updated model by which menthol may prove hedonic, satiate thirst and affect ventilation will also be presented, with the potential performance implications of these findings discussed and modelled. Qualitative reflections from athletes that have implemented menthol mouth swilling in competition, training and maximal exercise will also be included