Automated detection of hypoglycemia-induced EEG changes recorded by subcutaneous electrodes in subjects with type 1 diabetes--the brain as a biosensor

Minimally invasive surgery for placement of a subcutaneous EEG implant

Background

A new class of subcutaneous electroencephalography has enabled ultra long-term monitoring of people with epilepsy. The objective of this paper is to describe surgeons' experiences in an early series of implantations as well as discomfort or complications experienced by the participants.

Methods

We included 38 implantation procedures from two trials on people with epilepsy and healthy adults. Questionnaires to assess surgeons' and participants' experience were analyzed as well as all recorded adverse events occurring up to 21 days post-surgery.

Results

With training, the implantation could be performed in approximately 15 min. Overall, the implantation procedure was considered easy to perform with only 2 episodes where the implant got fixated in the introducing needle and a new implant had to be used. The explantation procedure was considered effortless. In 2 cases the silicone sheath covering the lead was damaged during the explantation, but it was possible to remove the entire implant without leaving any foreign body under the skin. Especially in the trial on healthy participants, a proportion experienced adverse events in the form of headache or implant-pain up to 21 days post-operatively. In 6 cases, adverse events contributed to the decision to explant and discontinue the study: Four of these cases involved implant pain or headache; One case involved a post-operative local infection; and in one case superficial lead placement resulted in skin perforation a few weeks after implantation.

Conclusion

The implantation and explantation procedures are considered swift and easy to perform by both neurosurgeons and ENT surgeons. The implant is well tolerated by most participants. However, headache or pain around the implant can occur for up to 21 days post-operatively as anticipated with any such surgery. The expected benefits from the implant should always outweigh the potential disadvantages.

Sub-scalp electroencephalography: A next-generation technique to study human neurophysiology

Sub-scalp electroencephalography (ssEEG) is emerging as a promising technology in ultra-long-term electroencephalography (EEG) recordings. Given the diversity of devices available in this nascent field, uncertainty persists about its utility in epilepsy evaluation. This review critically dissects the many proposed utilities of ssEEG devices including (1) seizure quantification, (2) seizure characterization, (3) seizure lateralization, (4) seizure localization, (5) seizure alarms, (6) seizure forecasting, (7) biomarker discovery, (8) sleep medicine, and (9) responsive stimulation. The different ssEEG devices in development have individual design philosophies with unique strengths and limitations. There are devices offering primarily unilateral recordings (24/7 EEG^TM SubQ, Neuroview^TM, Soenia® UltimateEEG™), bilateral recordings (Minder™, Epios™), and even those with responsive stimulation capability (EASEE®). We synthesize the current knowledge of these ssEEG systems. We review the (1) ssEEG devices, (2) use case scenarios, (3) challenges and (4) suggest a roadmap for ideal ssEEG designs.

Hypoglycaemic events resembling focal seizures -A case report and literature review

PURPOSE

To review the literature, for cases of hypoglycaemia misdiagnosed as epilepsy, including our interesting case of a patient with Type 1 Diabetes Mellitus, diagnosed with focal epilepsy.

METHODS

A literature search was completed. 20 of 473 studies, with a total of 22 cases found using specified search terms were relevant to this review. The papers identified and reviewed were those that dealt with hypoglycaemia misdiagnosed as epilepsy. The majority are isolated case reports given the rarity of this entity.

RESULTS

An underlying insulinoma is the most common cause for hypoglycaemic episodes to be misdiagnosed as epilepsy. Early morning seizures were prominent in 9 of the 22 cases.

CONCLUSION

Although rare, hypoglycaemia is an important differential diagnosis for drug-resistant epilepsy and early morning events may be an indication. We report the first case of recurrent hypoglycaemia from exogenous insulin, misdiagnosed as focal epilepsy with an available video EEG. The unusual presentation appeared clinically indistinct from recurrent focal seizures.

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

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.

Detection of Hypoglycemia Using Measures of EEG Complexity in Type 1 Diabetes Patients

Previous literature has demonstrated that hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable scalp electroencephalography (EEG) changes in power spectral density. In the present study, we used a dataset of 19-channel scalp EEG recordings in 34 patients with T1D who underwent a hyperinsulinemic-hypoglycemic clamp study. We found that hypoglycemic events are also characterized by EEG complexity changes that are quantifiable at the single-channel level through empirical conditional and permutation entropy and fractal dimension indices, i.e., the Higuchi index, residuals, and tortuosity. Moreover, we demonstrated that the EEG complexity indices computed in parallel in more than one channel can be used as the input for a neural network aimed at identifying hypoglycemia and euglycemia. The accuracy was about 90%, suggesting that nonlinear indices applied to EEG signals might be useful in revealing hypoglycemic events from EEG recordings in patients with T1D.

The effect of miniaturization and galvanic separation of EEG sensor devices in an auditory attention detection task

Recent technological advances in the design of concealable miniature electroencephalography (mini-EEG) devices are paving the way towards 24/7 neuromonitoring applications in daily life. However, such mini-EEG devices only cover a small area and record EEG over much shorter inter- electrode distances than in traditional EEG headsets. These drawbacks can potentially be compensated for by deploying a multitude of such mini-EEG devices and then jointly processing their recorded EEG signals. In this study, we simulate and investigate the effect of using such multi-node EEG recordings in which the nodes are galvanically separated from each other, and only use their internal electrodes to make short- distance EEG recordings. We focus on a use-case in auditory attention detection (AAD), and we demonstrate that the AAD performance using galvanically separated short-distance EEG measurements is comparable to using an equal number of long- distance EEG measurements if in both cases the electrodes are optimally placed on the scalp. To this end, we use a channel selection method based on a modified version of the least absolute shrinkage and selection operator (LASSO) technique, viz. the group-LASSO, in order to find these optimal locations.

Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia

Hypoglycemia or low blood glucose is the most feared complication of insulin treatment of diabetes. For people with diabetes, the mismatch between the insulin therapy and the body's physiology could increase the risk of hypoglycemia. Nocturnal hypoglycemia is particularly dangerous for type-1 diabetes patients because its symptoms may obscure during sleep. The early onset detection of hypoglycemia at night time is necessary because it can result in unconsciousness and even death. This paper presents new electroencephalogram spectral features for nocturnal hypoglycemia detection. The system uses high-order spectral moments for feature extraction and Bayesian neural network for classification. From a clinical study of hypoglycemia of eight patients with type-1 diabetes at night, we find that these spectral moments of theta band and alpha band changed significantly. During hypoglycemia episodes, the theta moments increased significantly (P < 0.001) while the features of alpha band reduced significantly (P < 0.001). Using the optimal Bayesian neural network, the classification results were 85% and 52% in sensitivity and specificity, respectively. The significant correlation (P < 0.001) with real blood glucose profiles shows the effectiveness of the proposed features for the detection of nocturnal hypoglycemia.

Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection

OBJECTIVE

Concealable, miniaturized electroencephalography (mini-EEG) recording devices are crucial enablers toward long-term ambulatory EEG monitoring. However, the resulting miniaturization limits the inter-electrode distance and the scalp area that can be covered by a single device. The concept of wireless EEG sensor networks (WESNs) attempts to overcome this limitation by placing a multitude of these mini-EEG devices at various scalp locations. We investigate whether optimizing the WESN topology can compensate for miniaturization effects in an auditory attention detection (AAD) paradigm.

METHODS

Starting from standard full-cap high-density EEG data, we emulate several candidate mini-EEG sensor nodes that locally collect EEG data with embedded electrodes separated by short distances. We propose a greedy group-utility based channel selection strategy to select a subset of these candidate nodes to form a WESN. We compare the AAD performance of this WESN with the performance obtained using long-distance EEG recordings.

RESULTS

The AAD performance using short-distance EEG measurements is comparable to using an equal number of long-distance EEG measurements if, in both cases, the optimal electrode positions are selected. A significant increase in performance was found when using nodes with three electrodes over nodes with two electrodes.

CONCLUSION

When the nodes are optimally placed, WESNs do not significantly suffer from EEG miniaturization effects in the case of AAD.

SIGNIFICANCE

WESN-like platforms allow us to achieve similar AAD performance as with long-distance EEG recordings while adhering to the stringent miniaturization constraints for ambulatory EEG. Their applicability in an AAD task is important for the design of neuro-steered auditory prostheses.

Cognitive, neurophysiologic and metabolic sequelae of previous hypoglycemic coma revealed by hyperinsulinemic-hypoglycemic clamp in type 1 diabetic patients

To examine the relationship between electroencephalographic (EEG) activity and hypoglycemia unawareness, we investigated early parameters of vigilance and awareness of various symptom categories in response to hypoglycemia in intensively treated type 1 diabetic (T1DM) patients with different degrees of hypoglycemia unawareness. Hypoglycemia was induced with a hyperinsulinemic-hypoglycemic clamp in six T1DM patients with a history of hypoglycemia unawareness previous severe hypoglycemic coma (SH) and in six T1DM patients without (C) history of hypoglycemia unawareness previous severe hypoglycemic coma. Cognitive function tests (four choice reaction time), counterregulatory responses (adrenaline), and symptomatic responses were evaluated at euglycemia (90 mg/dl) and during step-wise plasma glucose reduction (68, 58 and 49 mg/dl). EEG activity was recorded continuously throughout the study and analyzed by spectral analysis. Cognitive function deteriorated significantly at a glucose threshold of 55 ± 1 mg/dl in both groups (p = ns) during hypoglycemia, while the glucose threshold for autonomic symptoms was significantly lower in SH patients than in C patients (49 ± 1 vs. 54 ± 1 mg/dl, p < 0.05, respectively). In SH patients, eye-closed resting EEG showed a correlation between the mean dominance frequency and plasma glucose (r = 0.62, p < 0.001). Theta relative power increased during controlled hypoglycemia compared to euglycemia (21.6 ± 6 vs. 15.5 ± 3% Hz p < 0.05) and was higher than in the C group (21.6 ± 6 vs. 13.8 ± 3%, p < 0.03). The cognitive task beta activity was lower in the SH group than in the C group (14.8 ± 3 Hz, vs. 22.6 ± 4 vs. p < 0.03). Controlled hypoglycemia elicits cognitive dysfunction in both C and SH patients; however, significant EEG alterations during hypoglycemia were detected mainly in patients with a history of hypoglycemia unawareness and previous severe hypoglycemic coma. These data suggest that prior episodes of hypoglycemic coma modulate brain electric activity.

Physiological artifacts in scalp EEG and ear-EEG

Background

A problem inherent to recording EEG is the interference arising from noise and artifacts. While in a laboratory environment, artifacts and interference can, to a large extent, be avoided or controlled, in real-life scenarios this is a challenge. Ear-EEG is a concept where EEG is acquired from electrodes in the ear.

Methods

We present a characterization of physiological artifacts generated in a controlled environment for nine subjects. The influence of the artifacts was quantified in terms of the signal-to-noise ratio (SNR) deterioration of the auditory steady-state response. Alpha band modulation was also studied in an open/closed eyes paradigm.

Results

Artifacts related to jaw muscle contractions were present all over the scalp and in the ear, with the highest SNR deteriorations in the gamma band. The SNR deterioration for jaw artifacts were in general higher in the ear compared to the scalp. Whereas eye-blinking did not influence the SNR in the ear, it was significant for all groups of scalps electrodes in the delta and theta bands. Eye movements resulted in statistical significant SNR deterioration in both frontal, temporal and ear electrodes. Recordings of alpha band modulation showed increased power and coherence of the EEG for ear and scalp electrodes in the closed-eyes periods.

Conclusions

Ear-EEG is a method developed for unobtrusive and discreet recording over long periods of time and in real-life environments. This study investigated the influence of the most important types of physiological artifacts, and demonstrated that spontaneous activity, in terms of alpha band oscillations, could be recorded from the ear-EEG platform. In its present form ear-EEG was more prone to jaw related artifacts and less prone to eye-blinking artifacts compared to state-of-the-art scalp based systems.

On the Keyhole Hypothesis: High Mutual Information between Ear and Scalp EEG

We propose and test the keyhole hypothesis—that measurements from low dimensional EEG, such as ear-EEG reflect a broadly distributed set of neural processes. We formulate the keyhole hypothesis in information theoretical terms. The experimental investigation is based on legacy data consisting of 10 subjects exposed to a battery of stimuli, including alpha-attenuation, auditory onset, and mismatch-negativity responses and a new medium-long EEG experiment involving data acquisition during 13 h. Linear models were estimated to lower bound the scalp-to-ear capacity, i.e., predicting ear-EEG data from simultaneously recorded scalp EEG. A cross-validation procedure was employed to ensure unbiased estimates. We present several pieces of evidence in support of the keyhole hypothesis: There is a high mutual information between data acquired at scalp electrodes and through the ear-EEG “keyhole,” furthermore we show that the view—represented as a linear mapping—is stable across both time and mental states. Specifically, we find that ear-EEG data can be predicted reliably from scalp EEG. We also address the reverse view, and demonstrate that large portions of the scalp EEG can be predicted from ear-EEG, with the highest predictability achieved in the temporal regions and when using ear-EEG electrodes with a common reference electrode.

Hypoglycemia-Associated EEG Changes Following Antecedent Hypoglycemia in Type 1 Diabetes Mellitus

BACKGROUND

Recurrent hypoglycemia has been shown to blunt hypoglycemia symptom scores and counterregulatory hormonal responses during subsequent hypoglycemia. We therefore studied whether hypoglycemia-associated electroencephalogram (EEG) changes are affected by an antecedent episode of hypoglycemia.

METHODS

Twenty-four patients with type 1 diabetes mellitus (10 with normal hypoglycemia awareness, 14 with hypoglycemia unawareness) were studied on 2 consecutive days by hyperinsulinemic glucose clamp at hypoglycemia (2.0-2.5 mmol/L) during a 1-h period. EEG was recorded, cognitive function assessed, and hypoglycemia symptom scores and counterregulatory hormonal responses were obtained.

RESULTS

Twenty-one patients completed the study. Hypoglycemia-associated EEG changes were identified on both days with no differences in power or frequency distribution in the theta, alpha, or the combined theta-alpha band during hypoglycemia on the 2 days. Similar degree of cognitive dysfunction was also present during hypoglycemia on both days. When comparing the aware and unaware group, there were no differences in the hypoglycemia-associated EEG changes. There were very subtle differences in cognitive function between the two groups on day 2. The symptom response was higher in the aware group on both days, while only subtle differences were seen in the counterregulatory hormonal response.

CONCLUSION

Antecedent hypoglycemia does not affect hypoglycemia-associated EEG changes in patients with type 1 diabetes mellitus.

Hypoglycemia-Induced Changes in the Electroencephalogram: An Overview

Hypoglycemia is defined by an abnormally low blood glucose level. The condition develops when rates of glucose entry into the systematic circulation are reduced relative to the glucose uptake by the tissues. A cardinal manifestation of hypoglycemia arises from inadequate supply of glucose to the brain, where glucose is the primary metabolic fuel. The brain is one of the first organs to be affected by hypoglycemia. Shortage of glucose in the brain, or neuroglycopenia, results in a gradual loss of cognitive functions causing slower reaction time, blurred speech, loss of consciousness, seizures, and ultimately death, as the hypoglycemia progresses. The electrical activity in the brain represents the metabolic state of the brain cells and can be measured by electroencephalography (EEG). An association between hypoglycemia and changes in the EEG has been demonstrated, although blood glucose levels alone do not seem to predict neuroglycopenia. This review provides an overview of the current literature regarding changes in the EEG during episodes of low blood glucose.

Hypoglycemia-Associated EEG Changes in Prepubertal Children With Type 1 Diabetes

BACKGROUND

The purpose of this study was to explore the possible difference in the electroencephalogram (EEG) pattern between euglycemia and hypoglycemia in children with type 1 diabetes (T1D) during daytime and during sleep. The aim is to develop a hypoglycemia alarm based on continuous EEG measurement and real-time signal processing.

METHOD

Eight T1D patients aged 6-12 years were included. A hyperinsulinemic hypoglycemic clamp was performed to induce hypoglycemia both during daytime and during sleep. Continuous EEG monitoring was performed. For each patient, quantitative EEG (qEEG) measures were calculated. A within-patient analysis was conducted comparing hypoglycemia versus euglycemia changes in the qEEG. The nonparametric Wilcoxon signed rank test was performed. A real-time analyzing algorithm developed for adults was applied.

RESULTS

The qEEG showed significant differences in specific bands comparing hypoglycemia to euglycemia both during daytime and during sleep. In daytime the EEG-based algorithm identified hypoglycemia in all children on average at a blood glucose (BG) level of 2.5 ± 0.5 mmol/l and 18.4 (ranging from 0 to 55) minutes prior to blood glucose nadir. During sleep the nighttime algorithm did not perform.

CONCLUSIONS

We found significant differences in the qEEG in euglycemia and hypoglycemia both during daytime and during sleep. The algorithm developed for adults detected hypoglycemia in all children during daytime. The algorithm had too many false alarms during the night because it was more sensitive to deep sleep EEG patterns than hypoglycemia-related EEG changes. An algorithm for nighttime EEG is needed for accurate detection of nocturnal hypoglycemic episodes in children. This study indicates that a hypoglycemia alarm may be developed using real-time continuous EEG monitoring.

A method for quantitative assessment of artifacts in EEG, and an empirical study of artifacts

Wearable EEG systems for continuous brain monitoring is an emergent technology that involves significant technical challenges. Some of these are related to the fact that these systems operate in conditions that are far less controllable with respect to interference and artifacts than is the case for conventional systems. Quantitative assessment of artifacts provides a mean for optimization with respect to electrode technology, electrode location, electronic instrumentation and system design. To this end, we propose an artifact assessment method and evaluate it over an empirical study of 3 subjects and 5 different types of artifacts. The study showed consistent results across subjects and artifacts.

EEG power and glucose fluctuations are coupled during sleep in young adults with type 1 diabetes

OBJECTIVE

To determine the coupling between brain activity and glucose variations during sleep in young adults with type 1 diabetes mellitus (T1DM).

METHODS

27 participants, age 18-30, wore a continuous glucose monitoring system (CGMS) and underwent in-laboratory overnight polysomnography (PSG). Quantitative electroencephalogram (qEEG) metrics were determined from the PSG and included Delta, Theta, Alpha, Sigma, Beta and Gamma Band power at 5-min intervals. Wavelet Coherence Analysis was employed to determine the time varying and frequency specific coupling between glucose and EEG Band power. ANOVA was used to compare differences across fluctuation speeds and EEG bands.

RESULTS

There was a high degree of time varying and frequency specific coupling between glucose variations and EEG power in all EEG Bands during sleep. The average number of intervals of statistically significant coherence was highest for fluctuations periods between 10 and 30min in all Bands (p<0.0001 for each). Mean significant coherence was negatively correlated with hemoglobin A1c, a marker of glycemic control.

CONCLUSIONS

The relationship between glucose and EEG power during sleep is time varying and frequency dependent in young adults with T1DM.

SIGNIFICANCE

Understanding the time varying mutual relationship between glucose changes and brain activity during sleep may have implications for disease management in T1DM.

Non-EEG based ambulatory seizure detection designed for home use: What is available and how will it influence epilepsy care?

OBJECTIVE

This study aimed to (1) evaluate available systems and algorithms for ambulatory automatic seizure detection and (2) discuss benefits and disadvantages of seizure detection in epilepsy care.

METHODS

PubMed and EMBASE were searched up to November 2014, using variations and synonyms of search terms "seizure prediction" OR "seizure detection" OR "seizures" AND "alarm".

RESULTS

Seventeen studies evaluated performance of devices and algorithms to detect seizures in a clinical setting. Algorithms detecting generalized tonic-clonic seizures (GTCSs) had varying sensitivities (11% to 100%) and false alarm rates (0.2-4/24 h). For other seizure types, detection rates were low, or devices produced many false alarms. Five studies externally validated the performance of four different devices for the detection of GTCSs. Two devices were promising in both children and adults: a mattress-based nocturnal seizure detector (sensitivity: 84.6% and 100%; false alarm rate: not reported) and a wrist-based detector (sensitivity: 89.7%; false alarm rate: 0.2/24 h).

SIGNIFICANCE

Detection of seizure types other than GTCSs is currently unreliable. Two detection devices for GTCSs provided promising results when externally validated in a clinical setting. However, these devices need to be evaluated in the home setting in order to establish their true value. Automatic seizure detection may help prevent sudden unexpected death in epilepsy or status epilepticus, provided the alarm is followed by an effective intervention. Accurate seizure detection may improve the quality of life (QoL) of subjects and caregivers by decreasing burden of seizure monitoring and may facilitate diagnostic monitoring in the home setting. Possible risks are occurrence of alarm fatigue and invasion of privacy. Moreover, an unexpectedly high seizure frequency might be detected for which there are no treatment options. We propose that future studies monitor benefits and disadvantages of seizure detection systems with particular emphasis on QoL, comfort, and privacy of subjects and impact of false alarms.

Non-invasive, home-based electroencephalography hypoglycaemia warning system for personal monitoring using skin surface electrodes: a single-case feasibility study

AIMS

Hypoglycaemia unawareness is a common condition associated with increased risk of severe hypoglycaemia. The purpose of the authors' study was to develop a simple to use, home-based and non-invasive hypoglycaemia warning system based on electroencephalography (EEG), and to demonstrate its use in a single-case feasibility study.

METHODS

A participant with type 1 diabetes forms a single-person case study where blood sugar levels and EEG were recorded. EEG was recorded using skin surface electrodes placed behind the ear located within the T3 region by the participant in the home. EEG was analysed retrospectively to develop an algorithm which would trigger a warning if EEG changes associated with hypoglycaemia onset were detected.

RESULTS

All hypoglycaemia events were detected by the EEG hypoglycaemia warning algorithm. Warnings were triggered with blood glucose concentration levels at or below 4.2 mmol/l in this participant and no warnings were issued when in euglycaemia.

CONCLUSION

The feasibility of a non-invasive EEG-based hypoglycaemia warning system for personal monitoring in the home has been demonstrated in a single case study. The results suggest that further studies are warranted to evaluate the system prospectively in a larger group of participants.

Hypoglycemia-Induced Decrease of EEG Coherence in Patients with Type 1 Diabetes

BACKGROUND

Hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable electroencephalography (EEG) changes. Previous studies have, however, evaluated these changes on a single EEG channel level, whereas multivariate analysis of several EEG channels has been scarcely investigated. The aim of the present work is to use a coherence approach to quantitatively assess how hypoglycemia affects mutual connectivity of different brain areas.

MATERIALS AND METHODS

EEG multichannel data were obtained from 19 patients with T1D (58% males; mean age, 55 ± 2.4 years; diabetes duration, 28.5 ± 2.6 years; glycated hemoglobin, 8.0 ± 0.2%) who underwent a hyperinsulinemic-hypoglycemic clamp study. The information partial directed coherence (iPDC) function was computed through multivariate autoregressive models during eu- and hypoglycemia in the theta and alpha bands.

RESULTS

In passing from eu- to hypoglycemia, absolute values of the iPDC function tend to decrease in both bands in all combinations of the considered channels. In particular, the scalar indicator [Formula: see text], which summarizes iPDC information, significantly decreased (P < 0.01) in 17 of 19 subjects: from T5-A1A2 to C3-A1A2 from O1-A1A2 to C4-A1A2 and from O2-A1A2 to Cz-A1A2 in the theta band and from O1-A1A2 to T4-A1A2 and from O1-A1A2 to C4-A1A2 in the alpha band.

CONCLUSIONS

The coherence decrease measured by iPDC in passing from eu- to hypoglycemia is likely related to the progressive loss of cognitive function and altered cerebral activity in hypoglycemia. This result encourages further quantitative investigation of EEG changes in hypoglycemia and of how EEG acquisition and real-time processing can support hypoglycemia alert systems.

Combining genetic algorithm and Levenberg-Marquardt algorithm in training neural network for hypoglycemia detection using EEG signals

Hypoglycemia is the most common but highly feared complication induced by the intensive insulin therapy in patients with type 1 diabetes mellitus (T1DM). Nocturnal hypoglycemia is dangerous because sleep obscures early symptoms and potentially leads to severe episodes which can cause seizure, coma, or even death. It is shown that the hypoglycemia onset induces early changes in electroencephalography (EEG) signals which can be detected non-invasively. In our research, EEG signals from five T1DM patients during an overnight clamp study were measured and analyzed. By applying a method of feature extraction using Fast Fourier Transform (FFT) and classification using neural networks, we establish that hypoglycemia can be detected efficiently using EEG signals from only two channels. This paper demonstrates that by implementing a training process of combining genetic algorithm and Levenberg-Marquardt algorithm, the classification results are improved markedly up to 75% sensitivity and 60% specificity on a separate testing set.

Hypoglycemia-associated changes in the electroencephalogram in patients with type 1 diabetes and normal hypoglycemia awareness or unawareness

Hypoglycemia is associated with increased activity in the low-frequency bands in the electroencephalogram (EEG). We investigated whether hypoglycemia awareness and unawareness are associated with different hypoglycemia-associated EEG changes in patients with type 1 diabetes. Twenty-four patients participated in the study: 10 with normal hypoglycemia awareness and 14 with hypoglycemia unawareness. The patients were studied at normoglycemia (5-6 mmol/L) and hypoglycemia (2.0-2.5 mmol/L), and during recovery (5-6 mmol/L) by hyperinsulinemic glucose clamp. During each 1-h period, EEG, cognitive function, and hypoglycemia symptom scores were recorded, and the counterregulatory hormonal response was measured. Quantitative EEG analysis showed that the absolute amplitude of the θ band and α-θ band up to doubled during hypoglycemia with no difference between the two groups. In the recovery period, the θ amplitude remained increased. Cognitive function declined equally during hypoglycemia in both groups and during recovery reaction time was still prolonged in a subset of tests. The aware group reported higher hypoglycemia symptom scores and had higher epinephrine and cortisol responses compared with the unaware group. In patients with type 1 diabetes, EEG changes and cognitive performance during hypoglycemia are not affected by awareness status during a single insulin-induced episode with hypoglycemia.

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.

Decrease of EEG Coherence during hypoglycemia in type 1 diabetic subjects

Hypoglycemic events have been proven to be associated with measurable EEG changes. Several works in the literature have evaluated these changes by considering approaches at the single EEG channel level, but multivariate analyses have been scarcely investigated in Type 1 diabetes (T1D) subjects. The aim of the present work is to assess if and how hypoglycemia affects EEG coherence in a subset of EEG channels acquired in a hospital setting where eye- and muscle activation-induced artifacts are virtually absent. In particular, EEG multichannel data, acquired in 19 T1D hospitalized subjects undertaken to an insulin-induced hypoglycemia experiment, are considered. Computation of Partial Directed Coherence (PDC) through multivariate autoregressive models of P3-A1A2, P4-A1A2, C3-A1A2 and C4-A1A2 EEG channels shows that a decrease in the value of coherence, most likely related to the progressive loss of cognitive function and altered cerebral activity, occurs when passing from eu- to hypoglycemia, in both theta ([4, 8] Hz) and alpha ([8, 13] Hz) bands.

Hypoglycemia-related electroencephalogram changes assessed by multiscale entropy

BACKGROUND

Several clinical studies have shown that low blood glucose (BG) levels affect electroencephalogram (EEG) rhythms through the quantification of traditional indicators based on linear spectral analysis. Nonlinear measures used in the last decades to characterize the EEG in several physiopathological conditions have never been assessed in hypoglycemia. The present study investigates if properties of the EEG signal measured by nonlinear entropy-based algorithms are altered in a significant manner when a state of hypoglycemia is entered.

SUBJECTS AND METHODS

EEG was acquired from 19 patients with type 1 diabetes during a hyperinsulinemic-euglycemic-hypoglycemic clamp experiment. In parallel, BG was frequently monitored by the standard YSI glucose and lactate analyzer and used to identify two 1-h intervals corresponding to euglycemia and hypoglycemia, respectively. In each subject, the P3-C3 EEG derivation in the two glycemic intervals was assessed using the multiscale entropy (MSE) approach, obtaining measures of sample entropy (SampEn) at various temporal scales. The comparison of how signal irregularity measured by SampEn varies as the temporal scale increases in the two glycemic states provides information on how EEG complexity is affected by hypoglycemia.

RESULTS

For both glycemic states, the MSE analysis showed that SampEn increases at small time scales and then monotonically decreases as the time scale becomes larger. Comparing the two conditions, SampEn was higher in hypoglycemia only at medium time scales.

CONCLUSIONS

A decrease in the complexity of EEG occurs when a state of hypoglycemia is entered, because of a degradation of the EEG long-range temporal correlations. Thanks to its ability to assess nonlinear dynamics of the EEG signal, the MSE approach seems to be a useful tool to complement information brought by standard linear indicators and provide new insights on how hypoglycemia affects brain functioning.

Optimal target range for blood glucose in hyperglycaemic patients in a neurocritical care unit

BACKGROUND

Hyperglycaemia is common among patients with critical neurological injury, even if they have no history of diabetes. The optimal target range for normalizing their blood glucose is unknown.

METHODS

Retrospective data were extracted from 890 hyperglycaemic individuals (glucose > 200 mg/dL) admitted to neuroscience critical care unit (NCCU) and these patients were divided into two groups: intensive glucose control group with target glucose of < 140 mg/dL achieved and moderate control with glucose levels 140-180 mg/dL. The groups were also stratified according to the hyperglycaemia type (pre-existing diabetes or stress-related). We defined the primary endpoint as death from any cause during NCCU admission.

RESULTS

In NCCU, tighter control of blood glucose at ≤ 140 mg/dL was associated with increased, mortality of individuals with pre-existing diabetes compared with moderate control [29 of 310 patients (9.4%) vs 15 of 304 patients (4.9%), p = 0.034]. Patient age [adjusted odds ratio (OR) = 1.12; 95% confidence interval (CI) = 1.05-1.19; p < 0.001], level of glycated haemoglobin (adjusted OR = 1.24; 95% CI = 1.04-1.48; p = 0.017) and hypoglycaemia (adjusted OR = 10.3; 95% CI = 2.92-36.6; p < 0.001) were positively associated with higher mortality. Death rate was lower among stress-related hyperglycaemic patients with tighter glucose controlled at ≤ 140 mg/dL [6 of 140 patients (4.3%) vs 15 of 136 patients (11.0%), p = 0.035].

CONCLUSION

A differential association is evident between glucose levels and mortality in diabetes and stress-related hyperglycaemia patients. However, given the observational nature of our work, no clinical recommendations can be given and prospective studies are required to further investigate these findings.

Analyzing EEG signals under insulin-induced hypoglycemia in type 1 diabetes patients

Hypoglycemia is dangerous and considered as a limiting factor of the glycemic control therapy for patients with type 1 diabetes mellitus (T1DM). Nocturnal hypoglycemia is especially feared because early warning symptoms are unclear during sleep so an episode of hypoglycemia may lead to a fatal effect on patients. The main objective of this paper is to explore the correlation between hypoglycemia and electroencephalography (EEG) signals. To do this, the EEG of five T1DM adolescents from an overnight insulin-induced study is analyzed by spectral analysis to extract four different parameters. We aim to explore the response of these parameters during the clamp study which includes three main phases of normal, hypoglycemia and recovery. We also look at data at the blood glucose level (BGL) of 3.3-3.9 mmol/l to find a threshold to distinguish between non-hypoglycemia and hypoglycemia states. The results show that extracted EEG parameters are highly correlated with patients' conditions during the study. It is also shown that at the BGL of 3.3 mmol/l, responses to hypoglycemia in EEG signals start to significantly occur.

Nocturnal continuous glucose and sleep stage data in adults with type 1 diabetes in real-world conditions

BACKGROUND

Sleep plays an important role in health, and poor sleep is associated with negative impacts on diabetes management, but few studies have objectively evaluated sleep in adults with type 1 diabetes mellitus (T1DM). Nocturnal glycemia and sleep characteristics in T1DM were evaluated using body-worn sensors in real-world conditions.

METHODS

Analyses were performed on data collected by the Diabetes Management Integrated Technology Research Initiative pilot study of 17 T1DM subjects: 10 male, 7 female; age 19-61 years; T1DM duration 14.9 ± 11.0 years; hemoglobin A1c (HbA1c) 7.3% ± 1.3% (mean ± standard deviation). Each subject was equipped with a continuous glucose monitor and a wireless sleep monitor (WSM) for four nights. Sleep stages [rapid eye movement (REM), light, and deep sleep] were continuously recorded by the WSM. Nocturnal glycemia (mg/dl) was evaluated as hypoglycemia (<50 mg/dl), low (50-69 mg/dl), euglycemia (70-120 mg/dl), high (121-250 mg/dl), and hyperglycemia (>250 mg/dl) and by several indices of glycemic variability. Glycemia was analyzed within each sleep stage.

RESULTS

Subjects slept 358 ± 48 min per night, with 85 ± 27 min in REM sleep, 207 ± 42 min in light sleep, and 66 ± 30 min in deep sleep (mean ± standard deviation). Increased time in deep sleep was associated with lower HbA1c (R2 = 0.42; F = 9.37; p < .01). Nocturnal glycemia varied widely between and within subjects. Glycemia during REM sleep was hypoglycemia 5.5% ± 18.1%, low 6.6% ± 18.5%, euglycemia 44.6% ± 39.5%, high 37.9% ± 39.7%, and hyperglycemia 5.5% ± 21.2%; glycemia during light sleep was hypoglycemia 4.8% ± 12.4%, low 7.3% ± 12.9%, euglycemia 42.1% ± 33.7%, high 39.2% ± 34.6%, and hyperglycemia 6.5% ± 20.5%; and glycemia during deep sleep was hypoglycemia 0.5% ± 2.2%, low 5.8% ± 14.3%, euglycemia 48.0% ± 37.5%, high 39.5% ± 37.6%, and hyperglycemia 6.2% ± 19.5%. Significantly less time was spent in the hypoglycemic range during deep sleep compared with light sleep (p = .02).

CONCLUSIONS

Increased time in deep sleep was associated with lower HbA1c, and less hypoglycemia occurred in deep sleep in T1DM, though this must be further evaluated in larger subsequent studies. Furthermore, the consumer-grade WSM device was useful for objectively studying sleep in a real-world setting.

Fasting glucose level is associated with nocturnal hypoglycemia in elderly male patients with type 2 diabetes

BACKGROUND

Nocturnal hypoglycemia was a common and serious problem among patients with type 2 diabetes (T2DM), especially in the elderly. This study investigated whether fasting glucose was an indicator of nocturnal hypoglycemia in elderly male patients with T2DM.

METHODS

A total of 291 elderly male type 2 diabetic patients who received continuous glucose monitoring (CGM) between January 2007 and January 2011 were enrolled in the study. The association of fasting glucose and nocturnal hypoglycemia based on CGM data was analyzed, comparing with bedtime glucose.

RESULTS

Based on CGM data, patients with nocturnal hypoglycemia had significantly lower fasting glucose (5.88 ± 1.29 versus 6.92 ± 1.32 mmol/L) and bedtime glucose (7.33 ± 1.70 versus 8.01 ± 1.95 mmol/L) than patients without nocturnal hypoglycemia (both p < 0.01). Compared with the highest quartile, the lowest quartile of fasting glucose had a significantly increased risk of nocturnal hypoglycemia after the multiple adjustments (pfor trend < 0.001). However, this association did not appear in bedtime glucose. When the prediction of nocturnal hypoglycemia either by fasting glucose or bedtime glucose using the area under receiver operating characteristic (ROC) curve, fasting glucose but not bedtime glucose, was an indicator of nocturnal hypoglycemia, with an area under the ROC curve (AUC) of 0.714 (95% CI: 0.653 ∼ 0.774, p < 0.001). On the ROC curve, the Youden index was maximal when fasting glucose was 6.1 mmol/L.

CONCLUSIONS

Fasting glucose may be a convenient and clinically useful indicator of nocturnal hypoglycemia in elderly male patients with T2DM. Risk of nocturnal hypoglycemia significantly increased when fasting glucose was less than 6.1 mmol/L.

Hypoglycemia-associated electroencephalogram and electrocardiogram changes appear simultaneously

BACKGROUND

Tight glycemic control in type 1 diabetes mellitus (T1DM) may be accomplished only if severe hypoglycemia can be prevented. Biosensor alarms based on the body's reactions to hypoglycemia have been suggested. In the present study, we analyzed three lead electrocardiogram (ECG) and single-channel electroencephalogram (EEG) in T1DM patients during hypoglycemia.

MATERIALS AND METHODS

Electrocardiogram and EEG recordings during insulin-induced hypoglycemia in nine patients were used to assess the presence of ECG changes by heart rate, and estimates of QT interval (QTc) and time from top of T wave to end of T wave corrected for heartbeat interval and EEG changes by extraction of the power of the signal in the delta, theta, and alpha bands. These six features were assessed continuously to determine the time between changes and severe hypoglycemia.

RESULTS

QT interval changes and EEG theta power changes were detected in six and eight out of nine subjects, respectively. Rate of false positive calculations was one out of nine subjects for QTc and none for EEG theta power. Detection time medians (i.e., time from significant changes to termination of experiments) was 13 and 8 min for the EEG theta power and QTc feature, respectively, with no significant difference (p = .25).

CONCLUSIONS

Severe hypoglycemia is preceded by changes in both ECG and EEG features in most cases. Electroencephalogram theta power may be superior with respect to timing, sensitivity, and specificity of severe hypoglycemia detection. A multiparameter algorithm that combines data from different biosensors might be considered.

Hypoglycemia-related electroencephalogram changes are independent of gender, age, duration of diabetes, and awareness status in type 1 diabetes

INTRODUCTION

Neuroglycopenia in type 1 diabetes mellitus (T1DM) results in reduced cognition, unconsciousness, seizures, and possible death. Characteristic changes in the electroencephalogram (EEG) can be detected even in the initial stages. This may constitute a basis for a hypoglycemia alarm device. The aim of the present study was to explore the characteristics of the EEG differentiating normoglycemia and hypoglycemia and to elucidate potential group differences.

METHODS

We pooled data from experiments in T1DM where EEG was available during both normoglycemia and hypo-glycemia for each subject. Temporal EEG was analyzed by quantitative electroencephalogram (qEEG) analysis with respect to absolute amplitude and centroid frequency of the delta, theta, alpha, and beta bands, and the peak frequency of the unified theta-alpha band. To elucidate possible group differences, data were subsequently stratified by age group (± 50 years), gender, duration of diabetes (± 20 years), and hypoglycemia awareness status (normal/impaired awareness of hypoglycemia).

RESULTS

An increase in the log amplitude of the delta, theta, and alpha band and a decrease in the alpha band centroid frequency and the peak frequency of the unified theta-alpha band constituted the most significant hypoglycemia indicators (all p < .0001). The size of these qEEG changes remained stable across all strata.

CONCLUSIONS

Hypoglycemia-associated EEG changes remain stable across age group, gender, duration of diabetes, and hypoglycemia awareness status. This indicates that it may be possible to establish a general algorithm for hypoglycemia detection based on EEG measures.

Electroencephalography to assess motor control during balance tasks in people with diabetes

BACKGROUND

Balance is sensed through peripheral and central receptors and mediated by central control through the brain and spinal cord. Although some evidence exists as to the areas of the brain involved and how processing of data occurs in young individuals, nothing has been published on people with diabetes. The purpose of this study was to examine the electroencephalogram (EEG) during common sensorimotor and balance training tasks and to relate these to task difficulty.

SUBJECTS AND METHODS

Postural sway and EEG change of alpha, beta, and sigma wave bands were measured in 17 young subjects, 10 older subjects, and 10 subjects with diabetes during eight progressively more difficult balance tasks with eyes open and closed, feet in tandem or apart, and on foam or a firm surface.

RESULTS

EEG power of beta and sigma wave bands showed significant increases on the cortical and parietal areas of the brain relative to the control tasks when eyes were open (P<0.05). The cortical involvement decreased as the task became more difficult with vision and somatosensory information reduced, whereas that of the parietal area increased with task difficulty. The greatest increase was in subjects with diabetes, and the least was in younger people. Individuals with diabetes had increased sigma and beta EEG power in all regions of the brain examined with increased complexity of the balance task.

CONCLUSIONS

This study demonstrated cortical and parietal involvement in static balance tasks commonly used in sensorimotor training. The results support the proposal that there was increased subcortical control with increase in task difficulty in the young subjects, but in subjects with diabetes, there was a major increase in activity across the brain.

Detection of hypoglycemia associated EEG changes during sleep in type 1 diabetes mellitus

OBJECTIVE

Nocturnal hypoglycemia is a feared complication to insulin treated diabetes. Impaired awareness of hypoglycemia (IAH) increases the risk of severe hypoglycemia. EEG changes are demonstrated during daytime hypoglycemia. In this explorative study, we test the hypothesis that specific hypoglycemia-associated EEG-changes occur during sleep and are detectable in time for the patient to take action.

RESEARCH DESIGN AND METHODS

Ten patients with type 1 diabetes (duration 23.7 years) with IAH were exposed to insulin-induced hypoglycemia during the daytime and during sleep. EEG was recorded and analyzed real-time by an automated multi-parameter algorithm. Participants received an auditory alarm when EEG changes met a predefined threshold, and were instructed to consume a meal.

RESULTS

Seven out of eight participants developed hypoglycemia-associated EEG changes during daytime. During sleep, nine out of ten developed EEG changes (mean BG 2.0 mmol/l). Eight were awakened by the alarm. Four corrected hypoglycemia (mean BG 2.2 mmol/l), while four (mean BG 1.9 mmol/l) received glucose infusion. Two had false alarms. EEG-changes occurred irrespective of sleep stage. Post hoc improvement indicates the possibility of earlier detection of hypoglycemia.

CONCLUSIONS

Continuous EEG monitoring and automated real-time analysis may constitute a novel technique for a hypoglycemia alarm in patients with IAH.

Identification of hypoglycemic states for patients with T1DM using various parameters derived from EEG signals

For patients with Type 1 Diabetes Mellitus (T1DM), hypoglycemia is a very common but dangerous complication which can lead to unconsciousness, coma and even death. The variety of hypoglycemia symptoms is originated from the inadequate supply of glucose to the brain. In this study, we explore the connection between hypoglycemic episodes and the electrical activity of neurons within the brain or electroencephalogram (EEG) signals. By analyzing EEG signals from a clinical study of five children with T1DM, associated with hypoglycemia at night, we find that some EEG parameters change significantly under hypoglycemia condition. Based on these parameters, a method of detecting hypoglycemic episodes using EEG signals with a feed-forward multi-layer neural network is proposed. In our application, the classification results are 72% sensitivity and 55% specificity when the EEG signals are acquired from 2 electrodes C3 and O2. Furthermore, signals from different channels are also analyzed to observe the contributions of each channel to the performance of hypoglycemia classification.