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. © 2013 IEEE

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

A Self-Attention Deep Neural Network Regressor for real time blood glucose estimation in paediatric population using physiological signals

With the advent of modern digital technology, the physiological signals (such as electrocardiogram) are being acquired from portable wearable devices which are being used for non-invasive chronic disease management (such as Type 1 Diabetes). The diabetes management requires real-time assessment of blood glucose which is cumbersome for paediatric population due to clinical complexity and invasiveness. Therefore, real-time non-invasive blood glucose estimation is now pivotal for effective diabetes management. In this paper, we propose a Self-Attention Deep Neural Network Regressor for real-time non-invasive blood glucose estimation for paediatric population based on automatically extracted beat morphology. The first stage performs Morphological Extractor based on Self-Attention based Long Short-Term Memory driven by Convolutional Neural Network for highlighting local features based on temporal context. The second stage is based on Morphological Regressor driven by multilayer perceptron with dropout and batch normalization to avoid overfitting. We performed feature selection via logit model followed by Spearman's correlation among features to avoid feature redundancy. We trained as tested our model on publicly available MIT/BIH-Physionet databases and physiological signals acquired from a T1D paediatric population. We performed our evaluation via Clarke's Grid error to analyse estimation accuracy on range of blood values under different glycaemic conditions. The results show that our tool outperformed existing regression models with 89% accuracy under clinically acceptable range. The proposed model based on beat morphology significantly outperformed models based on HRV features

A self-attention deep neural network regressor for real time blood glucose estimation in paediatric population using physiological signals

With the advent of modern digital technology, the physiological signals (such as electrocardiogram) are being acquired from portable wearable devices which are being used for non-invasive chronic disease management (such as Type 1 Diabetes). The diabetes management requires real-time assessment of blood glucose which is cumbersome for paediatric population due to clinical complexity and invasiveness. Therefore, real-time non-invasive blood glucose estimation is now pivotal for effective diabetes management. In this paper, we propose a Self-Attention Deep Neural Network Regressor for real-time non-invasive blood glucose estimation for paediatric population based on automatically extracted beat morphology. The first stage performs Morphological Extractor based on Self-Attention based Long Short-Term Memory driven by Convolutional Neural Network for highlighting local features based on temporal context. The second stage is based on Morphological Regressor driven by multilayer perceptron with dropout and batch normalization to avoid overfitting. We performed feature selection via logit model followed by Spearman’s correlation among features to avoid feature redundancy. We trained as tested our model on publicly available MIT/BIH-Physionet databases and physiological signals acquired from a T1D paediatric population

Furthering the understanding of interstitial glucose on ECG metrics in people with type 1 diabetes

Cardiovascular autonomic neuropathy is a common complication of type 1 diabetes and can be considered the leading cause of mortality. It is increasingly important to detect early ECG alterations in healthy individuals with type 1 diabetes to help prevent the future onset of cardiovascular autonomic neuropathy and reduce mortality. This thesis aims to further understand the effect interstitial glucose has on ECG parameters during an eight-hour resting period, a novel low-intensity twenty-watt exercise test and a six-hour nocturnal period in individuals with type 1 diabetes. This thesis is a secondary analysis study including sixteen individuals absent of disease with generally well-controlled type 1 diabetes. Results revealed a reduction in QT (ms), QTc (ms) and HF (ms2) during the low-intensity exercise test during hyperglycaemia compared to euglycaemia. The increased rate of decline in interstitial glucose reduced heart rate (bpm), rMSSD (ms) and pNN50 (%) during the 6-hour nocturnal period. This thesis concludes that interstitial glucose results in some ECG alterations during parasympathetic withdrawal and the nocturnal period in a healthy disease-free cohort of individuals with type 1 diabetes. There was no effect of relatively similar interstitial glucose levels on ECG parameters during the 8-hour resting period

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

Hypoglycemia and Hyperglycemia are both serious diseases related to diabetes mellitus. Among Type 1 Diabetic patients, there are who experience both hypoglycemic and hyperglycemic events. The aim of this study was to identify of hypoglycemia and hyperglycemia based on ECG changes in this population. An ECG Acquisition and Analysis System based on LabVIEW software has been developed for collecting ECG signals and extracting features with abnormal changes. ECG parameters included Heart rate (HR), corrected QT interval (QTc), PR interval, corrected RT interval (RTc) and corrected TpTe interval (TpTeC). The results indicated that hypoglycemic and hyperglycemic states produce significant inverse changes on those ECG parameters