Position Statement on the Use of Medical Cannabis for the Treatment of Epilepsy in Canada.

In Canada, recreational use of cannabis was legalized in October 2018. This policy change along with recent publications evaluating the efficacy of cannabis for the medical treatment of epilepsy and media awareness about its use have increased the public interest about this agent. The Canadian League Against Epilepsy Medical Therapeutics Committee, along with a multidisciplinary group of experts and Canadian Epilepsy Alliance representatives, has developed a position statement about the use of medical cannabis for epilepsy. This article addresses the current Canadian legal framework, recent publications about its efficacy and safety profile, and our understanding of the clinical issues that should be considered when contemplating cannabis use for medical purposes.

Énoncé de position quant à l'utilisation du cannabis médical dans le traitement de l'épilepsie. L'utilisation du cannabis à des fins récréatives a été légalisée au Canada en octobre 2018. Parallèlement à ce changement de politique, de récentes publication visant à évaluer l'efficacité du cannabis dans le traitement de l'épilepsie, de même qu'une sensibilisation médiatique accrue en ce qui concerne son utilisation, ont eu pour effet d'augmenter l'intérêt du grand public à son égard. Le Comité médical thérapeutique de la Ligue canadienne contre l'épilepsie (LCCE), de concert avec un groupe multidisciplinaire d'experts et des représentants de l'Alliance canadienne de l'épilepsie, a ainsi élaboré un énoncé de position en ce qui regarde l'utilisation du cannabis médical dans le traitement de l'épilepsie. Cet article entend donc aborder le cadre légal qui prévaut actuellement au Canada et examiner de récentes publications s'étant penchées sur le profil sécuritaire et sur l'efficacité du cannabis. De plus, nous voulons apporter un éclairage au sujet des aspects cliniques dont il faudrait tenir compte au moment d'envisager l'utilisation du cannabis à des fins médicales.

Canadian epileptologists' counseling of drivers amidst guideline inconsistencies.

BACKGROUND: Epilepsy is a common medical condition for which physicians perform driver fitness assessments. The Canadian Medical association (CMA) and the Canadian Council of Motor transportation administrators (CCMTA) publish documents to guide Canadian physicians' driver fitness assessments. OBJECTIVES: We aimed to measure the consistency of driver fitness counseling among epileptologists in Canada, and to determine whether inconsistencies between national guidelines are associated with greater variability in counseling instructions. METHODS: We surveyed 35 epileptologists in Canada (response rate 71%) using a questionnaire that explored physicians' philosophies about driver fitness assessments and counseling practices of seizure patients in common clinical scenarios. Of the nine scenarios, CCMTA and CMA recommendations were concordant for only two. Cumulative agreement for all scenarios was calculated using Kappa statistic. Agreement for concordant (two) vs. discordant (seven) scenarios were split at the median and analyzed using the Wilcoxon signed rank sum test. RESULTS: Overall the agreement between respondents for the clinical scenarios was not acceptable (Kappa=0.28). For the two scenarios where CMa and CCMta guidelines were concordant, specialists had high levels of agreement with recommendations (89% each). A majority of specialists disagreed with CMa recommendations in three of seven discordant scenarios. The lack of consistency in respondents' agreement attained statistical significance (p<0.001). CONCLUSIONS: Canadian epileptologists have variable counseling practices about driving, and this may be attributable to inconsistencies between CMa and CCMta medical fitness guidelines. This study highlights the need to harmonize driving recommendations in order to prevent physician and patient confusion about driving fitness in Canada.

Immunomodulation in adult epilepsy: the role of IVIG.

Much of the research for intravenous immunoglobulins (IVIG) use in epilepsy has focused on childhood epilepsies and the results have been inconclusive. As evidence for inflammation in epilepsy and epileptogenesis is accumulating, IVIG might have a role to play in adult epilepsy. Our literature review focuses on the purported mechanisms of IVIG, the link between inflammation and the various causes of adult epilepsy and the different steps of epileptogenesis at which inflammation might play a role. We also review the current clinical evidence supporting IVIG as a treatment for epilepsy in the adult population. Though there is interesting theoretical potential for treatment of refractory epilepsy in adults with IVIG, insufficient evidence exists to support its standard use. The question remains if IVIG should still be considered as an end-of-the-line option for patients with epilepsy poorly responsive to all other treatments.

Effect of Training on Visual Identification of High Frequency Oscillations-A Delphi-Style Intervention.

OBJECTIVE: We examined the effect of a simple Delphi-method feedback on visual identification of high frequency oscillations (HFOs) in the ripple (80-250 Hz) band, and assessed the impact of this training intervention on the interrater reliability and generalizability of HFO evaluations. METHODS: We employed a morphology detector to identify potential HFOs at two thresholds and presented them to visual reviewers to assess the probability of each epoch containing an HFO. We recruited 19 board-certified epileptologists with various levels of experience to complete a series of HFO evaluations during three sessions. A Delphi-style intervention was used to provide feedback on the performance of each reviewer relative to their peers. A delayed-intervention paradigm was used, in which reviewers received feedback either before or after the second session. ANOVAs were used to assess the effect of the intervention on the reviewers' evaluations. Generalizability theory was used to assess the interrater reliability before and after the intervention. RESULTS: The intervention, regardless of when it occurred, resulted in a significant reduction in the variability between reviewers in both groups (p GroupDI = 0.037, p GroupEI = 0.003). Prior to the delayed-intervention, the group receiving the early intervention showed a significant reduction in variability (p GroupEI = 0.041), but the delayed-intervention group did not (p GroupDI = 0.414). Following the intervention, the projected number of reviewers required to achieve strong generalizability decreased from 35 to 16. SIGNIFICANCE: This study shows a robust effect of a Delphi-style intervention on the interrater variability, reliability, and generalizability of HFO evaluations. The observed decreases in HFO marking discrepancies across 14 of the 15 reviewers are encouraging: they are necessarily associated with an increase in interrater reliability, and therefore with a corresponding decrease in the number of reviewers required to achieve strong generalizability. Indeed, the reliability of all reviewers following the intervention was similar to that of experienced reviewers prior to intervention. Therefore, a Delphi-style intervention could be implemented either to sufficiently train any reviewer, or to further refine the interrater reliability of experienced reviewers. In either case, a Delphi-style intervention would help facilitate the standardization of HFO evaluations and its implementation in clinical care.

Predicting epileptic seizures in scalp EEG based on a variational Bayesian Gaussian mixture model of zero-crossing intervals.

A novel patient-specific seizure prediction method based on the analysis of positive zero-crossing intervals in scalp electroencephalogram (EEG) is proposed. In a moving-window analysis, the histogram of these intervals for the current EEG epoch is computed, and the values corresponding to specific bins are selected as an observation. Then, the set of observations from the last 5 min is compared with two reference sets of data points (preictal and interictal) through novel measures of similarity and dissimilarity based on a variational Bayesian Gaussian mixture model of the data. A combined index is then computed and compared with a patient-specific threshold, resulting in a cumulative measure which is utilized to form an alarm sequence for each channel. Finally, this channel-based information is used to generate a seizure prediction alarm. The proposed method was evaluated using ∼ 561 h of scalp EEG including a total of 86 seizures in 20 patients. A high sensitivity of 88.34 % was achieved with a false prediction rate of 0.155 h⁻¹ and an average prediction time of 22.5 min for the test dataset. The proposed method was also tested against a Poisson-based random predictor.

Electroencephalography in mesial temporal lobe epilepsy: a review.

Electroencephalography (EEG) has an important role in the diagnosis and classification of epilepsy. It can provide information for predicting the response to antiseizure drugs and to identify the surgically remediable epilepsies. In temporal lobe epilepsy (TLE) seizures could originate in the medial or lateral neocortical temporal region, and many of these patients are refractory to medical treatment. However, majority of patients have had excellent results after surgery and this often relies on the EEG and magnetic resonance imaging (MRI) data in presurgical evaluation. If the scalp EEG data is insufficient or discordant, invasive EEG recording with placement of intracranial electrodes could identify the seizure focus prior to surgery. This paper highlights the general information regarding the use of EEG in epilepsy, EEG patterns resembling epileptiform discharges, and the interictal, ictal and postictal findings in mesial temporal lobe epilepsy using scalp and intracranial recordings prior to surgery. The utility of the automated seizure detection and computerized mathematical models for increasing yield of non-invasive localization is discussed. This paper also describes the sensitivity, specificity, and predictive value of EEG for seizure recurrence after withdrawal of medications following seizure freedom with medical and surgical therapy.

Detection of epileptic seizures in scalp electroencephalogram: an automated real-time wavelet-based approach.

This study evaluates a new automated patient-specific method for epileptic seizure detection using scalp electroencephalogram (EEG). The method relies on a normalized wavelet-based index, named the combined seizure index (CSI), and requires a seizure example and a nonseizure EEG interval as reference. The CSI is derived for every epoch in each EEG channel and is sensitive to both the rhythmicity and relative energy of that epoch and the consistency of EEG patterns among different channels. Increasing significantly as seizures occur, the CSI is monitored using a one-sided cumulative sum test to generate appropriate alarms in each channel. A seizure alarm is finally generated according to channel-based information. The proposed method was evaluated using the scalp EEG test data of approximately 236 hours from 26 patients with a total of 79 focal seizures, achieving a high sensitivity of approximately 91% with a false detection rate of 0.33 per hour and a median detection latency of 7 seconds. In addition, statistical analysis revealed that the average CSI around the onset on the side of the focus in patients with temporal lobe epilepsy (TLE) is significantly greater than that of the opposite side (P < 0.001), indicating the capability of this index in lateralizing the seizure focus in this type of epilepsy.

Epileptic seizure prediction using variational mixture of Gaussians.

We propose a novel patient-specific method for predicting epileptic seizures by analysis of positive zero-crossing intervals in scalp electroencephalogram (EEG). In real-time analysis, the histogram of these intervals for the current EEG epoch is computed, and the values which correspond to the bins discriminating between interictal and preictal references are selected as an observation. Then, the set of observations from the last 5 min is compared with two reference sets of data points (interictal and preictal) using a variational Gaussian mixture model (GMM) of the data, and a combined index is computed. Comparing this index with a patient-specific threshold, an alarm sequence is produced for each channel. Finally, a seizure prediction alarm is generated according to channel-based information. The proposed method was evaluated using ~40.3 h of scalp EEG recordings from 6 patients with total of 28 partial seizures. A high sensitivity of 95% was achieved with a false prediction rate of 0.134/h and an average prediction time of 22.8 min for the test dataset.

Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform.

A novel wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG is proposed. In a moving-window analysis, the EEG from each channel is decomposed by wavelet packet transform. Using wavelet coefficients from seizure and nonseizure references, a patient-specific measure is developed to quantify the separation between seizure and nonseizure states for the frequency range of 1-30 Hz. Utilizing this measure, a frequency band representing the maximum separation between the two states is determined and employed to develop a normalized index, called combined seizure index (CSI). CSI is derived for each epoch of every EEG channel based on both rhythmicity and relative energy of that epoch as well as consistency among different channels. Increasing significantly during ictal states, CSI is inspected using one-sided cumulative sum test to generate proper channel alarms. Analyzing alarms from all channels, a seizure alarm is finally generated. The algorithm was tested on scalp EEG recordings from 14 patients, totaling approximately 75.8 h with 63 seizures. Results revealed a high sensitivity of 90.5%, a false detection rate of 0.51 h(-1) and a median detection delay of 7 s. The algorithm could also lateralize the focus side for patients with temporal lobe epilepsy.

Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG.

A novel real-time patient-specific algorithm to predict epileptic seizures is proposed. The method is based on the analysis of the positive zero-crossing intervals in the scalp electroencephalogram (EEG), describing the brain dynamics. In a moving-window analysis, the histogram of these intervals in each EEG epoch is computed, and the distribution of the histogram value in specific bins, selected using interictal and preictal references, is estimated based on the values obtained from the current epoch and the epochs of the last 5 min. The resulting distribution for each selected bin is then compared to two reference distributions (interictal and preictal), and a seizure prediction index is developed. Comparing this index with a patient-specific threshold for all EEG channels, a seizure prediction alarm is finally generated. The algorithm was tested on approximately 15.5 hours of multichannel scalp EEG recordings from three patients with temporal lobe epilepsy, including 14 seizures. 86% of seizures were predicted with an average prediction time of 20.8 min and a false prediction rate of 0.12/hr.

An entropy-based approach to predict seizures in temporal lobe epilepsy using scalp EEG.

We describe a novel algorithm for the prediction of epileptic seizures using scalp EEG. The method is based on the analysis of the positive zero-crossing interval series of the EEG signal and its first and second derivatives as a measure of brain dynamics. In a moving-window analysis, we estimated the probability density of these intervals and computed the differential entropy. The resultant entropy time series were then inspected using the cumulative sum (CUSUM) procedure to detect decreases as precursors of upcoming seizures. In the next step, the alarm sequences resulting from analysis of the EEG waveform and its derivatives were combined. Finally, a seizure prediction index was generated based on the spatio-temporal processing of the combined CUSUM alarms. We evaluated our algorithm using a dataset of approximately 21.5 hours of multichannel scalp EEG recordings from four patients with temporal lobe epilepsy, resulting in 87.5% sensitivity, a false prediction rate of 0.28/hr, and an average prediction time of 25 min.

A novel wavelet-based index to detect epileptic seizures using scalp EEG signals.

In this paper, we propose a novel wavelet-based algorithm for the detection of epileptic seizures. The algorithm is based on the recognition of rhythmic activities associated with ictal states in surface EEG recordings. Using a moving-window analysis, we first decomposed each EEG segment into a wavelet packet tree. Then, we extracted the coefficients corresponding to the frequency band of interest defined for rhythmic activities. Finally, a normalized index sensitive to both the rhythmicity and energy of the EEG signal was derived, based on the resulting coefficients. In our study, we evaluated this combined index for real-time detection of epileptic seizures using a dataset of approximately 11.5 hours of multichannel scalp EEG recordings from three patients and compared it to our previously proposed wavelet-based index. In this dataset, the novel combined index detected all epileptic seizures with a false detection rate of 0.52/hr.