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.

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.

Subdural to subgaleal EEG signal transmission: the role of distance, leakage and insulating affectors.

OBJECTIVE: To estimate the area of cortex affecting the extracranial EEG signal. METHODS: The coherence between intra- and extracranial EEG channels were evaluated on at least 10 min of spontaneous, awake data from seven patients admitted for epilepsy surgery work up. RESULTS: Cortical electrodes showed significant extracranial coherent signals in an area of approximately 150 cm(2) although the field of vision was probably only 31 cm(2) based on spatial averaging of intracranial channels taking into account the influence of the craniotomy and the silastic membrane of intracranial grids. Selecting the best cortical channels, it was possible to increase the coherence values compared to the single intracranial channel with highest coherence. The coherence seemed to increase linearly with an accumulation area up to 31 cm(2), where 50% of the maximal coherence was obtained accumulating from only 2 cm(2) (corresponding to one channel), and 75% when accumulating from 16 cm(2). CONCLUSION: The skull is an all frequency spatial averager but dominantly high frequency signal attenuator. SIGNIFICANCE: An empirical assessment of the actual area of cerebral sources generating the extracranial EEG provides better opportunities for clinical electroencephalographers to determine the location of origin of particular patterns in the EEG.

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.

Channel selection for automatic seizure detection.

OBJECTIVE: To investigate the performance of epileptic seizure detection using only a few of the recorded EEG channels and the ability of software to select these channels compared with a neurophysiologist. METHODS: Fifty-nine seizures and 1419 h of interictal EEG are used for training and testing of an automatic channel selection method. The characteristics of the seizures are extracted by the use of a wavelet analysis and classified by a support vector machine. The best channel selection method is based upon maximum variance during the seizure. RESULTS: Using only three channels, a seizure detection sensitivity of 96% and a false detection rate of 0.14/h were obtained. This corresponds to the performance obtained when channels are selected through visual inspection by a clinical neurophysiologist, and constitutes a 4% improvement in sensitivity compared to seizure detection using channels recorded directly on the epileptic focus. CONCLUSIONS: Based on our dataset, automatic seizure detection can be done using only three EEG channels without loss of performance. These channels should be selected based on maximum variance and not, as often done, using the focal channels. SIGNIFICANCE: With this simple automatic channel selection method, we have shown a computational efficient way of making automatic seizure detection.

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.