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

Diurnal Differences in Risk of Cardiac Arrhythmias during Spontaneous Hypoglycemia in Young People with Type 1 Diabetes

OBJECTIVE Hypoglycemia may exert proarrhythmogenic effects on the heart via sympathoadrenal stimulation and hypokalemia. Hypoglycemia-induced cardiac dysrhythmias are linked to the “dead-in-bed syndrome,” a rare but devastating condition. We examined the effect of nocturnal and daytime clinical hypoglycemia on electrocardiogram (ECG) in young people with type 1 diabetes. RESEARCH DESIGN AND METHODS Thirty-seven individuals with type 1 diabetes underwent 96 h of simultaneous ambulatory ECG and blinded continuous interstitial glucose monitoring (CGM) while symptomatic hypoglycemia was recorded. Frequency of arrhythmias, heart rate variability, and cardiac repolarization were measured during hypoglycemia and compared with time-matched euglycemia during night and day. RESULTS A total of 2,395 h of simultaneous ECG and CGM recordings were obtained; 159 h were designated hypoglycemia and 1,355 h euglycemia. A median duration of nocturnal hypoglycemia of 60 min (interquartile range 40–135) was longer than daytime hypoglycemia of 44 min (30–70) (P = 0.020). Only 24.1% of nocturnal and 51.0% of daytime episodes were symptomatic. Bradycardia was more frequent during nocturnal hypoglycemia compared with matched euglycemia (incident rate ratio [IRR] 6.44 [95% CI 6.26, 6.63], P < 0.001). During daytime hypoglycemia, bradycardia was less frequent (IRR 0.023 [95% CI 0.002, 0.26], P = 0.002) and atrial ectopics more frequent (IRR 2.29 [95% CI 1.19, 4.39], P = 0.013). Prolonged QTc, T-peak to T-end interval duration, and decreased T-wave symmetry were detected during nocturnal and daytime hypoglycemia. CONCLUSIONS Asymptomatic hypoglycemia was common. We identified differences in arrhythmic risk and cardiac repolarization during nocturnal versus daytime hypoglycemia in young adults with type 1 diabetes. Our data provide further evidence that hypoglycemia is proarrhythmogenic

Autonomic cardiac regulation during spontaneous nocturnal hypoglycemia in children with type 1 diabetes

Hypoglycemia is the most common complication in insulin treated diabetes. Though mostly mild, it can be fatal in rare cases: It is hypothesized that hypoglycemia related QTc prolongation contributes to cardiac arrhythmia.; To evaluate influence of nocturnal hypoglycemia on QTc and heart rate variability (HRV) in children with T1D.; Children and adolescents with T1D for at least 6 months participated in an observational study using continuous glucose monitoring (CGM) and Holter electrocardiogram for five consecutive nights. Mean QTc was calculated for episodes of nocturnal hypoglycemia (<3.7 mmol/L) and compared to periods of the same duration preceding hypoglycemia. HRV (RMSSD, low and high frequency power LF and HF) was analyzed for different 15 min intervals: before hypoglycemia, onset of hypoglycemia, before/after nadir, end of hypoglycemia and after hypoglycemia.; Mean QTc during hypoglycemia was significantly longer compared to euglycemia (412 ± 15 vs. 405 ± 18 ms, p = 0.005). HRV changed significantly: RMSSD (from 88 ± 57 to 73 ± 43 ms) and HF (from 54 ± 17 to 47 ± 17nu) decreased from before hypoglycemia to after nadir, while heart rate (from 69 ± 9 to 72 ± 12 bpm) and LF (from 44 ± 17 to 52 ± 21 nu) increased (p = 0.04).; A QTc lengthening effect of nocturnal hypoglycemia in children with T1D was documented. HRV changes occurred even before detection of nocturnal hypoglycemia by CGM, which may be useful for hypoglycemia prediction

Cardiac autonomic regulation and repolarization during acute experimental hypoglycemia in Type 2 diabetes

Hypoglycemia is associated with increased cardiovascular mortality in trials of intensive therapy in type 2 diabetes (T2DM). We previously observed an increase in arrhythmias during spontaneous prolonged hypoglycemia in T2DM patients. Our aim was to examine changes in cardiac autonomic function and repolarization during sustained experimental hypoglycemia. Twelve adults with T2DM and eleven age, BMI-matched nondiabetic controls underwent paired hyperinsulinemic clamps separated by 4 weeks. Glucose was maintained at euglycemia (6.0mmol/L) or hypoglycemia (2.5mmol/L) for one hour. Heart rate, blood pressure, heart rate variability were assessed every thirty minutes and corrected QT (QTc) and T wave morphology every 60 minutes. Heart rate initially increased in T2DM participants but then fell towards baseline despite maintained hypoglycemia at 1 hour, accompanied by reactivation of vagal tone. In nondiabetic participants, vagal tone remained depressed during sustained hypoglycemia. Diabetic participants exhibited greater heterogeneity of repolarization during hypoglycemia as demonstrated by T wave symmetry and Principal Component Analysis (PCA) ratio compared with the nondiabetic group. Epinephrine levels during hypoglycemia were similar between groups. Cardiac autonomic regulation during hypoglycemia appears time-dependent. T2DM individuals demonstrate greater repolarization abnormalities for a given hypoglycemic stimulus despite comparable sympathoadrenal responses. These mechanisms could contribute to arrhythmias during clinical hypoglycemic episodes

Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes

© 2016 ISA Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM

Dysglycemia and arrhythmias

Disorders in glucose metabolism can be divided into three separate but interrelated domains, namely hyperglycemia, hypoglycemia, and glycemic variability. Intensive glycemic control in patients with diabetes might increase the risk of hypoglycemic incidents and glucose fluctuations. These three dysglycemic states occur not only amongst patients with diabetes, but are frequently present in other clinical settings, such as during critically ill. A growing body of evidence has focused on the relationships between these dysglycemic domains with cardiac arrhythmias, including supraventricular arrhythmias (primarily atrial fibrillation), ventricular arrhythmias (malignant ventricular arrhythmias and QT interval prolongation), and bradyarrhythmias (bradycardia and heart block). Different mechanisms by which these dysglycemic states might provoke cardiac arr-hythmias have been identified in experimental studies. A customized glycemic control strategy to minimize the risk of hyperglycemia, hypoglycemia and glucose variability is of the utmost importance in order to mitigate the risk of cardiac arrhythmias