Francisco Sales

Views of patients with epilepsy on seizure prediction devices

Patients views on the relevance, performance requirements, and implementation of seizure prediction devices have so far not been evaluated in a standardized form. We here report views of outpatients with uncontrolled epilepsy from the epilepsy centers at Freiburg, Germany, and Coimbra, Portugal, based on a questionnaire. Interest in the development of methods for seizure prediction both for warning and for closed-loop interventions is high. High sensitivity of prediction is regarded as more important than specificity. Short prediction time windows are preferred, but the indication of seizure-prone periods is also considered worthwhile. Only a few patients are, however, willing to wear EEG electrodes for signal acquisition on a long-term basis. These data support the view that seizure prediction is of high interest to patients with uncontrolled epilepsy. Improvements in the performance of presently available prediction algorithms and technical improvements in EEG recording will, however, be necessary to meet patients requirements.

The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients.

From the very beginning the seizure prediction community faced problems concerning evaluation, standardization, and reproducibility of its studies. One of the main reasons for these shortcomings was the lack of access to high‐quality long‐term electroencephalography (EEG) data. In this article we present the EPILEPSIAE database, which was made publicly available in 2012. We illustrate its content and scope. The EPILEPSIAE database provides long‐term EEG recordings of 275 patients as well as extensive metadata and standardized annotation of the data sets. It will adhere to the current standards in the field of prediction and facilitate reproducibility and comparison of those studies. Beyond seizure prediction, it may also be of considerable benefit for studies focusing on seizure detection, basic neurophysiology, and other fields.

Epileptic seizure predictors based on computational intelligence techniques

The ability of computational intelligence methods to predict epileptic seizures is evaluated in long-term EEG recordings of 278 patients suffering from pharmaco-resistant partial epilepsy, also known as refractory epilepsy. This extensive study in seizure prediction considers the 278 patients from the European Epilepsy Database, collected in three epilepsy centres: Hôpital Pitié-là-Salpêtrière, Paris, France; Universitätsklinikum Freiburg, Germany; Centro Hospitalar e Universitário de Coimbra, Portugal. For a considerable number of patients it was possible to find a patient specific predictor with an acceptable performance, as for example predictors that anticipate at least half of the seizures with a rate of false alarms of no more than 1 in 6 h (0.15 h⁻¹). We observed that the epileptic focus localization, data sampling frequency, testing duration, number of seizures in testing, type of machine learning, and preictal time influence significantly the prediction performance. The results allow to face optimistically the feasibility of a patient specific prospective alarming system, based on machine learning techniques by considering the combination of several univariate (single-channel) electroencephalogram features. We envisage that this work will serve as benchmark data that will be of valuable importance for future studies based on the European Epilepsy Database.

Slow modulations of high-frequency activity (40–140 Hz) discriminate preictal changes in human focal epilepsy

Recent evidence suggests that some seizures are preceded by preictal changes that start from minutes to hours before an ictal event. Nevertheless an adequate statistical evaluation in a large database of continuous multiday recordings is still missing. Here, we investigated the existence of preictal changes in long-term intracranial recordings from 53 patients with intractable partial epilepsy (in total 531 days and 558 clinical seizures). We describe a measure of brain excitability based on the slow modulation of high-frequency gamma activities (40–140 Hz) in ensembles of intracranial contacts. In prospective tests, we found that this index identified preictal changes at levels above chance in 13.2% of the patients (7/53), suggesting that results may be significant for the whole group (p < 0.05). These results provide a demonstration that preictal states can be detected prospectively from EEG data. They advance understanding of the network dynamics leading to seizure and may help develop novel seizure prediction algorithms.

Modeling epileptic brain states using EEG spectral analysis and topographic mapping.

Changes in the spatio-temporal behavior of the brain electrical activity are believed to be associated to epileptic brain states. We propose a novel methodology to identify the different states of the epileptic brain, based on the topographic mapping of the time varying relative power of delta, theta, alpha, beta and gamma frequency sub-bands, estimated from EEG. Using normalized-cuts segmentation algorithm, points of interest are identified in the topographic mappings and their trajectories over time are used for finding out relations with epileptogenic propagations in the brain. These trajectories are used to train a Hidden Markov Model (HMM), which models the different epileptic brain states and the transition among them. Applied to 10 patients suffering from focal seizures, with a total of 30 seizures over 497.3h of data, the methodology shows good results (an average point-by-point accuracy of 89.31%) for the identification of the four brain states--interictal, preictal, ictal and postictal. The results suggest that the spatio-temporal dynamics captured by the proposed methodology are related to the epileptic brain states and transitions involved in focal seizures.

Adaptive visual memory reorganization in right medial temporal lobe epilepsy.

Purpose: We investigated functional reorganization mechanisms of the human medial temporal lobe (MTL) for episodic memory, in patients suffering from medial temporal lobe epilepsy (MTLE) with hippocampal sclerosis (HS).

Anticipating the unobserved: Prediction of subclinical seizures

Subclinical seizures (SCS) have rarely been considered in the diagnosis and therapy of epilepsy and have not been systematically analyzed in studies on seizure prediction. Here, we investigate whether predictions of subclinical seizures are feasible and how their occurrence may affect the performance of prediction algorithms. Using the European database of long-term recordings of surface and invasive electroencephalography data, we analyzed the data from 21 patients with SCS, including in total 413 clinically manifest seizures (CS) and 3341 SCS. Based on the mean phase coherence we investigated the predictive performance of CS and SCS. The two types of seizures had similar prediction sensitivities. Significant performance was found considerably more often for SCS than for CS, especially for patients with invasive recordings. When analyzing false alarms triggered by predicting CS, a significant number of these false predictions were followed by SCS for 9 of 21 patients. Although currently observed prediction performance may not be deemed sufficient for clinical applications for the majority of the patients, it can be concluded that the prediction of SCS is feasible on a similar level as for CS and allows a prediction of more of the seizures impairing patients, possibly also reducing the number of false alarms that were in fact correct predictions of CS. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Anti-NMDA receptor encephalitis presenting with total insomnia — A case report

Fatal insomnia (FI) is the first diagnosis to be considered by most neurologists when approaching a patient presenting with total insomnia followed by personality and cognitive changes, disturbance of alertness, autonomic hyperactivation and movement abnormalities. We report the case of a 30 year-old male patient who presented with total insomnia followed by episodes of psychomotor restlessness resembling anxiety attacks. Twenty days later, he developed refractory convulsive status epilepticus with admission to Intensive Care Unit. He progressed to a state of reduced alertness and responsiveness, presenting periods of agitation with abnormal dyskinetic movements, periods of autonomic instability and central hypoventilation. Workup revealed antibodies against N-methyl-d-aspartate receptor (NMDAR). Immunotherapy treatment led to a very significant improvement with the patient presenting only slight frontal lobe dysfunction after one year of recovery. To the best of our knowledge this is the first report of a patient with anti-NMDAR encephalitis first presenting with total insomnia. Our aim is to alert that anti-NMDAR encephalitis must be considered in the differential diagnosis of FI, especially in sporadic cases. Distinguishing the two conditions is very important as, contrarily to the fatal disclosure of FI, anti-NMDAR encephalitis is potentially reversible with adequate treatment even after severe and prolonged disease.

A Realistic Seizure Prediction Study Based on Multiclass SVM

A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

Combining Energy and Wavelet Transform for Epileptic Seizure Prediction in an Advanced Computational System

Seizure prediction in epileptic patients will allow a deep improvement in their quality of life. In the paper a new method using energy relative measures in wavelet coefficients is proposed and tested in several patients. The results show the potential of the technique, but also its limitations, stressing the needs for further work in a larger number of patients, using multimodal information and an advanced database with a large features set to be used in seizure prediction An advanced computational framework is under development, using multisensorial information to build a large set of features to be used in a classification system supporting seizure prediction. This system is composed of two main parts the algorithms base and the database, briefly described.