Feasibility of online seizure detection with continuous EEG monitoring in the intensive care unit.

INTRODUCTION: Continuous EEG (cEEG) is of great interest in view of the reported high prevalence of non-convulsive seizures on intensive care units (ICUs). Here, we describe our experiences applying a seizure warning system using cEEG monitoring. METHODS: Fifty comatose ICU patients were included prospectively and monitored. Twenty-eight patients had post-anoxic encephalopathy (PAE) and 22 had focal brain lesions. A measure of neuronal interactions, synchronization likelihood, was calculated online over 10s EEG epochs and instances when the synchronization likelihood exceeded a threshold where marked as seizures. RESULTS: Five patients developed seizures. Our method detected seizures in three patients, in the other patients seizures were missed because of they were non-convulsive and had a focal character. The average false positive rate was 0.676/h. DISCUSSION: This is our first attempt to implement online seizure detection in the ICU. Despite problems with artifacts and that we missed focally oriented seizures, we succeeded in monitoring patients online. Given the relatively high occurrence of seizures, online seizure detection with cEEG merits further development for use in ICUs.

The relationship between structural and functional connectivity: graph theoretical analysis of an EEG neural mass model.

We investigated the relationship between structural network properties and both synchronization strength and functional characteristics in a combined neural mass and graph theoretical model of the electroencephalogram (EEG). Thirty-two neural mass models (NMMs), each representing the lump activity of reasonably large groups of interacting excitatory and inhibitory neurons, were reciprocally and excitatory coupled using random rewiring as described by Watts and Strogatz. Numerical analysis of the network revealed an abrupt transition towards a synchronized state as a function of increasing coupling strength alpha. Synchronization increased with increasing degree and decreasing regularity of the network. Parameters of the functional network showed a diverse dependency on structural connectivity: normalized clustering coefficient gamma and path length lambda increased with increasing alpha. For sufficiently large alpha, however, gamma decreased with increasing rewiring probability p, while lambda increased. Hence, a structured functional network exists despite the randomness of the underlying structural network. That is, patterns of functional connectivity are influenced by patterns of the corresponding structural level but do not necessarily agree with those.

Indications for network regularization during absence seizures: weighted and unweighted graph theoretical analyses.

Previous studies with intracranial recordings suggested that a more random spatial structure of functional brain networks could be related to seizure generation. Here, we studied whether similar network changes in weighted and unweighted networks can be found in generalized absence seizures recorded with surface EEG. We retrospectively selected EEG recordings of eleven children with absence seizures. The functional neural networks were characterized by calculating both coherence and synchronization likelihood (SL) between 21 EEG signals that were either broad band filtered (1-48 Hz) or filtered in different frequency bands. From both weighted and unweighted networks the clustering coefficient (C) and path length (L) were computed and compared to 500 random networks. We compared the ictal with the pre-ictal network structure. During absence seizures there was an increase of synchronization in all frequency bands, seen most clearly in the SL-based networks, and the functional network topology changed towards a more ordered pattern, with an increase of C/C-s and L/L-s. This study supports the hypothesis of functional neural network changes during absence seizures. The network became more regularized in weighted and unweighted analyses, when compared to the more randomized pre-ictal network configuration.

Inter-observer variability of the EEG diagnosis of seizures in comatose patients.

OBJECTIVE: To assess the inter-observer agreement of the electroencephalogram (EEG) diagnosis of (non-convulsive) seizures in comatose patients. DESIGN/SETTING/PATIENTS: Nine clinicians with different levels of experience in clinical neurophysiology were asked to evaluate in a strictly controlled way 90 epochs (10s each) of 30 EEG's of 23 comatose patients admitted to the intensive care unit (ICU). For each EEG clinicians had to decide whether there was an electrographic seizure or not. Furthermore, Young's EEG criteria for (non-convulsive) seizures were scored in detail for all EEG's. Agreement was determined by calculating kappa values. RESULTS: The inter-observer agreement of an EEG diagnosis of seizure was limited. The overall kappa score for the five experienced raters was 0.5, and the kappa score for less experienced raters was 0.29. Kappa values for the individual Young's criteria were highly variable, indicating discrepancies in the interpretation of specific phenomena. Especially, some types of periodic discharges gave rise to different interpretations. CONCLUSIONS: The EEG diagnosis of (non-convulsive) seizures in ICU patients is not very reliable, even when strict criteria such as proposed by Young are applied. There is a need for less ambiguous EEG criteria for (non-convulsive) seizures and status epilepticus.

Small-world networks and epilepsy: graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures.

OBJECTIVE: Neuronal networks with a so-called "small-world" topography (characterized by strong clustering in combination with short path lengths) are known to facilitate synchronization, and possibly seizure generation. We tested the hypothesis that real functional brain networks during seizures display small-world features, using intracerebral recordings of mesial temporal lobe seizures. METHODS: We used synchronization likelihood (SL) to characterize synchronization patterns in intracerebral EEG recordings of 7 patients for 5 periods of interest: interictal, before-, during- and after rapid discharges (in which the last two periods are ictal) and postictal. For each period, graphs (abstract network representations) were reconstructed from the synchronization matrix and characterized by a clustering coefficient C (measure of local connectedness) and a shortest path length L (measure of overall network integration). Results were also compared with those obtained from random networks. RESULTS: The neuronal network changed during seizure activity, with an increase of C and L most prominent in the alpha, theta and delta frequency bands during and after the seizure. CONCLUSIONS: During seizures, the neuronal network moves in the direction of a more ordered configuration (higher C combined with a slightly, but significantly, higher L) compared to the more randomly organized interictal network, even after correcting for changes in synchronization strength. SIGNIFICANCE: Analysis of neuronal networks during seizures may provide insight into seizure genesis and development.

First case of ataxia with isolated vitamin E deficiency in the Netherlands.

We present a 36-year-old Dutch woman who suffered from a progressive form of cerebellar ataxia since school age. In her childhood she was diagnosed with Friedreich's ataxia. Genetic analysis of the frataxin gene at 34 years of age, however, had revealed no abnormal GAA triplet expansion. We identified two point mutations in the alpha-tocopherol transport protein (alpha-TTP) gene on chromosome 8q13, and the diagnosis ataxia with isolated vitamin E deficiency (AVED) was made. This report illustrates the diagnosis AVED and its relation to vitamin E metabolism. It is important to evaluate previously made diagnoses when newly developed tests can be performed for confirmation.