Identification of hypoglycemia and hyperglycemia in type 1 diabetic patients using ECG parameters

Blood glucose estimation based on ECG signal

Successful self-management of diabetes requires Continuous Glucose Monitors (CGMs). These CGMs have several limitations such as being invasive, expensive and limited in terms of use. Many techniques, in vain, have been proposed to overcome these limitations. Nowadays, with the help of the Internet of Medical Things (IoMT) technologies, researchers are working to find alternative solutions. They succeed to predict hypoglycemia and hyperglycemia peaks using Electrocardiogram (ECG) signals. However, they failed to use it to estimate the Blood Glucose Concentration (BGC) directly and in real time. Three patients with 08 days of measurements from the D1namo dataset contributed to the study. A new technique has been proposed to estimate the BGC curves based on ECG signals. We used a convolutional neural network to segment the different regions of ECG signals as well as we extracted ECG features that were required for the next step. Then, five regression models have been employed to estimate BGC using as input sixth ECG parameters. We were able to segment the ECG signals with an accuracy of 94% using the convolutional neural network algorithm. The best performance among all simulated models was provided by Exponential Gaussian Process Regression (GPR) with Root Mean Squared Error (RMSE) values of 0.32, 0.41, 0.67 and R-squared (R²) values of 98%, 80%, and 70% for patients 01, 02 and 03 respectively. The method indicates the potential use of ECG wearable devices as non-invasive for continuous blood glucose monitoring, which is affordable and durable.

Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review

(1) Background: Diabetes mellitus (DM) is a chronic, metabolic disease characterized by elevated levels of blood glucose. Recently, some studies approached the diabetes care domain through the analysis of the modifications of cardiovascular system parameters. In fact, cardiovascular diseases are the first leading cause of death in diabetic subjects. Thanks to their cost effectiveness and their ease of use, electrocardiographic (ECG) and photoplethysmographic (PPG) signals have recently been used in diabetes detection, blood glucose estimation and diabetes-related complication detection. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (2) Method: We performed a systematic review based on articles that focus on the use of ECG and PPG signals in diabetes care. The search was focused on keywords related to the topic, such as "Diabetes", "ECG", "PPG", "Machine Learning", etc. This was performed using databases, such as PubMed, Google Scholar, Semantic Scholar and IEEE Xplore. This review's aim is to provide a detailed overview of all the published methods, from the traditional (non machine learning) to the deep learning approaches, to detect and manage diabetes using PPG and ECG signals. This review will allow researchers to compare and understand the differences, in terms of results, amount of data and complexity that each type of approach provides and requires. (3) Results: A total of 78 studies were included. The majority of the selected studies focused on blood glucose estimation (41) and diabetes detection (31). Only 7 studies focused on diabetes complications detection. We present these studies by approach: traditional, machine learning and deep learning approaches. (4) Conclusions: ECG and PPG analysis in diabetes care showed to be very promising. Clinical validation and data processing standardization need to be improved in order to employ these techniques in a clinical environment.

Hyperglycemia Identification Using ECG in Deep Learning Era

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration.

Non-invasive Monitoring of Three Glucose Ranges Based On ECG By Using DBSCAN-CNN

Autonomic nervous system (ANS) can maintain homeostasis through the coordination of different organs including heart. The change of blood glucose (BG) level can stimulate the ANS, which will lead to the variation of Electrocardiogram (ECG). Considering that the monitoring of different BG ranges is significant for diabetes care, in this paper, an ECG-based technique was proposed to achieve non-invasive monitoring with three BG ranges: low glucose level, moderate glucose level, and high glucose level. For this purpose, multiple experiments that included fasting tests and oral glucose tolerance tests were conducted, and the ECG signals from 21 adults were recorded continuously. Furthermore, an approach of fusing density-based spatial clustering of applications with noise and convolution neural networks (DBSCAN-CNN) was presented for ECG preprocessing of outliers and classification of BG ranges based ECG. Also, ECG's important information, which was related to different BG ranges, was graphically visualized. The result showed that the percentages of accurate classification were 87.94% in low glucose level, 69.36% in moderate glucose level, and 86.39% in high glucose level. Moreover, the visualization results revealed that the highlights of ECG for the different BG ranges were different. In addition, the sensitivity of prediabetes/diabetes screening based on ECG was up to 98.48%, and the specificity was 76.75%. Therefore, we conclude that the proposed approach for BG range monitoring and prediabetes/diabetes screening has potentials in practical applications.

Electroencephalogram Reactivity to Hyperglycemia in Patients with Type 1 Diabetes

This paper is concerned with a study of hyperglycemia on four patients with type 1 diabetes at night time. We investigated the association between hyperglycemic episodes and electroencephalogram (EEG) signals using data from the central and occipital areas. The power spectral density of the brain waves was estimated to compare the difference between hyperglycemia and euglycemia using the hyperglycemic threshold of 8.3 mmol/L. The statistical results showed that alpha and beta bands were more sensitive to hyperglycemic episodes than delta and theta bands. During hyperglycemia, whereas the alpha power increased significantly in the occipital lobe (P<0.005), the power of the beta band increased significantly in all observed channels (P<0.01). Using the Pearson correlation, we assessed the relationship between EEG signals and glycemic episodes. The estimated EEG power levels of the alpha band and the beta band produced a significant correlation against blood glucose levels (P<0.005). These preliminary results show the potential of using EEG signals as a biomarker to detect hyperglycemia.

Statistical and spectral analysis of ECG signal towards achieving non-invasive blood glucose monitoring

Background

Globally, the cases of diabetes mellitus (diabetes) have increased in the past three decades, and it is recorded as one of the leading cause of death. This epidemic is a metabolic condition where the body cannot regulate blood glucose, thereby leading to abnormally high blood sugar. Genetic condition plays a significant role to determine a person susceptibility to the condition, a sedentary lifestyle and an unhealthy diet are behaviour that supports the current global epidemic. The complication that arises from diabetes includes loss of vision, peripheral neuropathy, cardiovascular complications and so on. Victims of this condition require constant monitoring of blood glucose which is done by the pricking of the finger. This procedure is painful, inconvenient and can lead to disease infection. Therefore, it is important to find a way to measure blood glucose non-invasively to minimize or eliminate the disadvantages encountered with the usual monitoring of blood glucose.

Method

In this paper, we performed two experiments on 16 participants while electrocardiogram (ECG) data was continuously captured. In the first experiment, participants are required to consume 75 g of anhydrous glucose solution (oral glucose tolerance test) and the second experiment, no glucose solution was taken. We explored statistical and spectral analysis on HRV, HR, R-H, P-H, PRQ, QRS, QT, QTC and ST segments derived from ECG signal to investigate which segments should be considered for the possibility of achieving non-invasive blood glucose monitoring. In the statistical analysis, we examined the pattern of the data with the boxplot technique to reveal the change in the statistical properties of the data. Power spectral density estimation was adopted for the spectral analysis to show the frequency distribution of the data.

Results

HRV segment obtained a statistical score of 81% for decreasing pattern and HR segment have the same statistical score for increasing pattern among the participants in the first quartile, median and mean properties. While ST segment has a statistical score of 81% for decreasing pattern in the third quartile, QT segment has 81% for increasing pattern for the median. From a total change score of 6, ST, QT, PRQ, P-H, HR and HRV obtained 4, 5, 4, 5 and 6 respectively. For spectral analysis, HRV and HR segment scored 81 and 75% respectively. ST, QT, PRQ have 75, 62 and 68% respectively.

Conclusions

The results obtained demonstrate that HR, HRV, PRQ, QT and ST segments under a normal, healthy condition are affected by glucose and should be considered for modelling a system to achieve the possibility of non-invasive blood glucose measurement with ECG.

Detecting Hypoglycemia Incidents Reported in Patients' Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance

BACKGROUND

Improper dosing of medications such as insulin can cause hypoglycemic episodes, which may lead to severe morbidity or even death. Although secure messaging was designed for exchanging nonurgent messages, patients sometimes report hypoglycemia events through secure messaging. Detecting these patient-reported adverse events may help alert clinical teams and enable early corrective actions to improve patient safety.

OBJECTIVE

We aimed to develop a natural language processing system, called HypoDetect (Hypoglycemia Detector), to automatically identify hypoglycemia incidents reported in patients' secure messages.

METHODS

An expert in public health annotated 3000 secure message threads between patients with diabetes and US Department of Veterans Affairs clinical teams as containing patient-reported hypoglycemia incidents or not. A physician independently annotated 100 threads randomly selected from this dataset to determine interannotator agreement. We used this dataset to develop and evaluate HypoDetect. HypoDetect incorporates 3 machine learning algorithms widely used for text classification: linear support vector machines, random forest, and logistic regression. We explored different learning features, including new knowledge-driven features. Because only 114 (3.80%) messages were annotated as positive, we investigated cost-sensitive learning and oversampling methods to mitigate the challenge of imbalanced data.

RESULTS

The interannotator agreement was Cohen kappa=.976. Using cross-validation, logistic regression with cost-sensitive learning achieved the best performance (area under the receiver operating characteristic curve=0.954, sensitivity=0.693, specificity 0.974, F1 score=0.590). Cost-sensitive learning and the ensembled synthetic minority oversampling technique improved the sensitivity of the baseline systems substantially (by 0.123 to 0.728 absolute gains). Our results show that a variety of features contributed to the best performance of HypoDetect.

CONCLUSIONS

Despite the challenge of data imbalance, HypoDetect achieved promising results for the task of detecting hypoglycemia incidents from secure messages. The system has a great potential to facilitate early detection and treatment of hypoglycemia.

Evaluation of Hypoglycaemia with Non-Invasive Sensors in People with Type 1 Diabetes and Impaired Awareness of Hypoglycaemia

People with type 1 diabetes and impaired awareness of hypoglycaemia (IAH) are prone to severe hypoglycaemia. Previous attempts to develop non-invasive hypoglycaemia alarm systems have shown promising results, but it is not known if such alarms can detect severe hypoglycaemia in people with IAH. We aimed to explore whether a combination of non-invasive sensors could reliably evaluate hypoglycaemia (plasma glucose (PG) minimum 2.5 mmol/L) in people with IAH. Twenty participants with type 1 diabetes and IAH underwent randomly ordered, single blinded hyperinsulinemic euglycaemic and hyperinsulinemic hypoglycaemic clamps. Sweating, skin temperature, ECG, counterregulatory hormones and symptoms of hypoglycaemia were assessed. Overall, we were not able to detect clamp-induced hypoglycaemia with sufficient sensitivity and specificity for further clinical use. As a post-hoc analysis, we stratified participants according to their ability to identify hypoglycaemic symptoms during hypoglycaemic clamps. Five out of 20 participants could identify such symptoms. These participants had a significantly higher adrenaline response to hypoglycaemia (p < 0.001) and were reliably identified by sensors. Based on our observations, a non-invasive alarm system based on measurement of sweating responses and ECG changes during hypoglycaemia might provide an alert at a plasma glucose concentration around 2.5 mmol/L if an adequate sympatho-adrenal reaction is elicited.

Sensor Monitoring of Physical Activity to Improve Glucose Management in Diabetic Patients: A Review

Diabetic individuals need to tightly control their blood glucose concentration. Several methods have been developed for this purpose, such as the finger-prick or continuous glucose monitoring systems (CGMs). However, these methods present the disadvantage of being invasive. Moreover, CGMs have limited accuracy, notably to detect hypoglycemia. It is also known that physical exercise, and even daily activity, disrupt glucose dynamics and can generate problems with blood glucose regulation during and after exercise. In order to deal with these challenges, devices for monitoring patients’ physical activity are currently under development. This review focuses on non-invasive sensors using physiological parameters related to physical exercise that were used to improve glucose monitoring in type 1 diabetes (T1DM) patients. These devices are promising for diabetes management. Indeed they permit to estimate glucose concentration either based solely on physical activity parameters or in conjunction with CGM or non-invasive CGM (NI-CGM) systems. In these last cases, the vital signals are used to modulate glucose estimations provided by the CGM and NI-CGM devices. Finally, this review indicates possible limitations of these new biosensors and outlines directions for future technologic developments.

Effects of hyperglycemia on variability of RR, QT and corrected QT intervals in Type 1 diabetic patients

In this study, we evaluated the effects of hyperglycemia on the variability of RR (HRV), QT interval variability (QTV) and corrected QT interval variability (QTcV) during hyperglycemic and non-hyperglycemic conditions in six Type 1 diabetic patients at nights. The aim of this study was to investigate the association of high blood glucose levels with autonomic modulation of heart rate and variation in ventricular repolarization. Blood glucose level (BGL) threshold for defining hyperglycemia state was set at 8.33 mmol/l. Variability of RR, QT and corrected QT intervals during hyperglycemic and non-hyperglycemic were quantified using time and frequency domain measures. Hypomon® device was used to monitor ECG signals and acquire RR and QT intervals in Type 1 diabetic patients overnight. The results indicated that time and frequency domain HRV variables were significantly decreased under hyperglycemic condition and inversely correlated with BGL. QTV parameters also reduced when BGL increased and time domain measures of QTV were inversely associated with BGL. Variability in QTc interval was much less than in the QT interval and demonstrated a lower SDNN and LF power. We concluded that certain components of HRV, time-domain measures of QTV and QTc but not QTcV are strongly correlated to high blood glucose levels and can be good markers to identify hyperglycemic events in T1DM.

Hypo- and Hyperglycemic Alarms: Devices and Algorithms

Soon after the discovery that insulin regulates blood glucose by Banting and Best in 1922, the symptoms and risks associated with hypoglycemia became widely recognized. This article reviews devices to warn individuals of impending hypo- and hyperglycemia; biosignals used by these devices include electroencephalography, electrocardiography, skin galvanic resistance, diabetes alert dogs, and continuous glucose monitors (CGMs). While systems based on other technology are increasing in performance and decreasing in size, CGM technology remains the best method for both reactive and predictive alarming of hypo- or hyperglycemia.