Influence of Time-Series Normalization, Number of Nodes, Connectivity and Graph Measure Selection on Seizure-Onset Zone Localization from Intracranial EEG.

We investigated the influence of processing steps in the estimation of multivariate directed functional connectivity during seizures recorded with intracranial EEG (iEEG) on seizure-onset zone (SOZ) localization. We studied the effect of (i) the number of nodes, (ii) time-series normalization, (iii) the choice of multivariate time-varying connectivity measure: Adaptive Directed Transfer Function (ADTF) or Adaptive Partial Directed Coherence (APDC) and (iv) graph theory measure: outdegree or shortest path length. First, simulations were performed to quantify the influence of the various processing steps on the accuracy to localize the SOZ. Afterwards, the SOZ was estimated from a 113-electrodes iEEG seizure recording and compared with the resection that rendered the patient seizure-free. The simulations revealed that ADTF is preferred over APDC to localize the SOZ from ictal iEEG recordings. Normalizing the time series before analysis resulted in an increase of 25-35% of correctly localized SOZ, while adding more nodes to the connectivity analysis led to a moderate decrease of 10%, when comparing 128 with 32 input nodes. The real-seizure connectivity estimates localized the SOZ inside the resection area using the ADTF coupled to outdegree or shortest path length. Our study showed that normalizing the time-series is an important pre-processing step, while adding nodes to the analysis did only marginally affect the SOZ localization. The study shows that directed multivariate Granger-based connectivity analysis is feasible with many input nodes (> 100) and that normalization of the time-series before connectivity analysis is preferred.

Detection of generalized tonic-clonic seizures using surface electromyographic monitoring.

OBJECTIVE: A prospective multicenter phase III trial was undertaken to evaluate the performance and tolerability in the epilepsy monitoring unit (EMU) of an investigational wearable surface electromyographic (sEMG) monitoring system for the detection of generalized tonic-clonic seizures (GTCSs). METHODS: One hundred ninety-nine patients with a history of GTCSs who were admitted to the EMU in 11 level IV epilepsy centers for clinically indicated video-electroencephalographic monitoring also received sEMG monitoring with a wearable device that was worn on the arm over the biceps muscle. All recorded sEMG data were processed at a central site using a previously developed detection algorithm. Detected GTCSs were compared to events verified by a majority of three expert reviewers. RESULTS: For all subjects, the detection algorithm detected 35 of 46 (76%, 95% confidence interval [CI] = 0.61-0.87) of the GTCSs, with a positive predictive value (PPV) of 0.03 and a mean false alarm rate (FAR) of 2.52 per 24 h. For data recorded while the device was placed over the midline of the biceps muscle, the system detected 29 of 29 GTCSs (100%, 95% CI = 0.88-1.00), with a detection delay averaging 7.70 s, a PPV of 6.2%, and a mean FAR of 1.44 per 24 h. Mild to moderate adverse events were reported in 28% (55 of 199) of subjects and led to study withdrawal in 9% (17 of 199). These adverse events consisted mostly of skin irritation caused by the electrode patch that resolved without treatment. No serious adverse events were reported. SIGNIFICANCE: Detection of GTCSs using an sEMG monitoring device on the biceps is feasible. Proper positioning of this device is important for accuracy, and for some patients, minimizing the number of false positives may be challenging.

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

Electromyography-based seizure detector: Preliminary results comparing a generalized tonic-clonic seizure detection algorithm to video-EEG recordings.

OBJECTIVE: Automatic detection of generalized tonic-clonic seizures (GTCS) will facilitate patient monitoring and early intervention to prevent comorbidities, recurrent seizures, or death. Brain Sentinel (San Antonio, Texas, USA) developed a seizure-detection algorithm evaluating surface electromyography (sEMG) signals during GTCS. This study aims to validate the seizure-detection algorithm using inpatient video-electroencephalography (EEG) monitoring. METHODS: sEMG was recorded unilaterally from the biceps/triceps muscles in 33 patients (17white/16 male) with a mean age of 40 (range 14-64) years who were admitted for video-EEG monitoring. Maximum voluntary biceps contraction was measured in each patient to set up the baseline physiologic muscle threshold. The raw EMG signal was recorded using conventional amplifiers, sampling at 1,024 Hz and filtered with a 60 Hz noise detection algorithm before it was processed with three band-pass filters at pass frequencies of 3-40, 130-240, and 300-400 Hz. A seizure-detection algorithm utilizing Hotelling's T-squared power analysis of compound muscle action potentials was used to identify GTCS and correlated with video-EEG recordings. RESULTS: In 1,399 h of continuous recording, there were 196 epileptic seizures (21 GTCS, 96 myoclonic, 28 tonic, 12 absence, and 42 focal seizures with or without loss of awareness) and 4 nonepileptic spells. During retrospective, offline evaluation of sEMG from the biceps alone, the algorithm detected 20 GTCS (95%) in 11 patients, averaging within 20 s of electroclinical onset of generalized tonic activity, as identified by video-EEG monitoring. Only one false-positive detection occurred during the postictal period following a GTCS, but false alarms were not triggered by other seizure types or spells. SIGNIFICANCE: Brain Sentinel's seizure detection algorithm demonstrated excellent sensitivity and specificity for identifying GTCS recorded in an epilepsy monitoring unit. Further studies are needed in larger patient groups, including children, especially in the outpatient setting.

Ictal asystole: an indication for pacemaker implantation and emerging cause of sudden death.

Ictal asystole is being recognized as a potential mechanism of sudden unexplained death in epilepsy (SUDEP). We report a case of a patient without known cardiac disease presenting with ictal asystole resulting in syncope, trauma, and need for pacemaker implantation. The management of ictal asystole is also briefly reviewed. This case is notable for the asystolic episode wholly captured on video-electroencephalogram/electrocardiogram, the serious risk of trauma and death posed to the patient, and its implications for the mechanism of ictal asystole. This report will alert physicians to the possibility of ictal arrhythmias as a cause of syncope and SUDEP in vulnerable patients.

Late-onset temporal lobe epilepsy in a patient with juvenile myoclonic epilepsy.

We report a patient with longstanding, severe juvenile myoclonic epilepsy who subsequently developed features of temporal lobe epilepsy, which gradually became clinically dominant. Over the years, there was an electrographic evolution from the typical generalised epileptiform patterns, characteristic of juvenile myoclonic epilepsy, to the novel appearance of interictal temporal spikes immediately preceding bisynchronous discharges, and subsequently to temporal intermittent rhythmic delta activity and temporal lobe-onset seizures. In this rare case of coexistent primary generalised epilepsy and focal epilepsy, the epileptic networks of the two forms of epilepsy appear to overlap.

Automated Processing of Single-Channel Surface Electromyography From Generalized Tonic-Clonic Seizures to Inform Semiology.

PURPOSE: Advances in surface electromyography (sEMG) monitoring allow for long-term data collection in a natural environment, giving objective information that may identify risk of sudden unexpected death in epilepsy and guide clinical decision-making. Generalized tonic-clonic seizure semiology, namely motor tonic and clonic phase duration, may be an important factor in determining the level of seizure control and risk of sudden unexpected death in epilepsy. This study demonstrates a quantitative analysis of sEMG collected with a dedicated wearable device. METHODS: During routine monitoring, 23 generalized tonic-clonic seizures from 19 subjects were simultaneously recorded with video-EEG and sEMG. A continuous wavelet-transform was used to determine the frequency components of sEMG recorded during generalized tonic-clonic seizures. An automated process, incorporating a variant of cross-validation, was created to identify ideal frequencies and magnitude ranges for tonic and clonic phases and determine phase durations. Phase durations determined using sEMG analysis were compared with phase durations determined by independent epileptologists' review of video-EEG. RESULTS: Cross-validation revealed that the optimal frequency bands for tonic and clonic phases are 150 to 270 Hz and 12 to 70 Hz, respectively. The average difference in phase duration calculated using the two methods for tonic and clonic phases and total seizure duration were -0.42 ± 4.94, -5.12 ± 9.68, and -5.11 ± 11.33 seconds, respectively (results presented are TsEMG - TvEEG, µ ± σ). CONCLUSIONS: The automated processing of sEMG presented here accurately identified durations of tonic, clonic, and total motor durations of generalized tonic-clonic seizures similar to durations identified by epileptologists' review of video-EEG.

Influence of Intracranial Electrode Density and Spatial Configuration on Interictal Spike Localization: A Case Study.

PURPOSE: Poor seizure outcomes after epilepsy surgery often reflect an incorrect localization of the epileptic sources by standard intracranial EEG interpretation because of limited electrode coverage of the epileptogenic zone. This study investigates whether, in such conditions, source modeling is able to provide more accurate source localization than the standard clinical method that can be used prospectively to improve surgical resection planning. METHODS: Suboptimal epileptogenic zone sampling is simulated by subsets of the electrode configuration used to record intracranial EEG in a patient rendered seizure free after surgery. sLORETA and the clinical method solutions are applied to interictal spikes sampled with these electrode subsets and are compared for colocalization with the resection volume and displacement due to electrode downsampling. RESULTS: sLORETA provides often congruent and at times more accurate source localization when compared with the standard clinical method. However, with electrode downsampling, individual sLORETA solution locations can vary considerably and shift consistently toward the remaining electrodes. CONCLUSIONS: sLORETA application can improve source localization based on the clinical method but does not reliably compensate for suboptimal electrode placement. Incorporating sLORETA solutions based on intracranial EEG in surgical planning should proceed cautiously in cases where electrode repositioning is planned on clinical grounds.

The N-methyl-d-aspartate receptor in heart development: a gene knockdown model using siRNA.

Antagonists of the N-methyl-d-aspartate receptor (NMDAR) may disrupt the development of the cardiac neural crest (CNC) and contribute to conotruncal heart defects. To test this interaction, a loss-of-function model was generated using small interfering RNAs (siRNA) directed against the critical NR1-subunit of this receptor in avian embryos. The coding sequence of the chicken NR1 gene and predicted protein sequences were characterized and found to be homologous with other vertebrate species. Analysis of its spatiotemporal expression demonstrated its expression within the neural tube at pre-migratory CNC sites. siRNA targeted to the NR1-mRNA in pre-migratory CNC lead to a significant decrease in NR1 protein expression. However, embryo survival and heart development were not adversely affected. These results indicate that the CNC may function normally in the absence of functional NMDAR, and that NMDAR antagonists may have a complex impact upon the CNC that transcends impairment of a single receptor type.