The evaluation of treatment-resistant epilepsy

Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification

Electroencephalogram (EEG) signals contain vital information on the electrical activities of the brain and are widely used to aid epilepsy analysis. A challenging element of epilepsy diagnosis, accurate classification of different epileptic states, is of particular interest and has been extensively investigated. A new deep learning-based classification methodology, namely epileptic EEG signal classification (EESC), is proposed in this paper. This methodology first transforms epileptic EEG signals to power spectrum density energy diagrams (PSDEDs), then applies deep convolutional neural networks (DCNNs) and transfer learning to automatically extract features from the PSDED, and finally classifies four categories of epileptic states (interictal, preictal duration to 30 min, preictal duration to 10 min, and seizure). It outperforms the existing epilepsy classification methods in terms of accuracy and efficiency. For instance, it achieves an average classification accuracy of over 90% in a case study with CHB-MIT epileptic EEG data.

Drug-resistant epilepsy classified by a phenotyping algorithm associates with NTRK2

OBJECTIVE

Up to 40% of patients with epilepsy become drug resistant (DRE). Genetic factors are likely to play a role. While efforts have focused on the transporter and target hypotheses, neither of them fully explains the pan-pharmacoresistance seen in DRE.

MATERIALS AND METHODS

In this study, we developed and used a phenotyping algorithm for the identification of DRE, responders, and epilepsy-free controls that were sequenced using a gene panel developed by the Pharmacogenomics Research Network (PGRN), which includes 82 genes involved in drug response. We tested the transporter hypothesis of DRE, the association between drug resistance and variants in the ATP-binding cassette family of genes previously associated with DRE, and also investigated potential new genetic factors.

RESULTS

In the analysis of DRE vs controls, NTRK2 was significantly associated with DRE (rs76950094; P = 1.19 × 10^-7 and gene-based P-value = 1.67 × 10^-4 ). NTRK2 encodes TrkB, which is involved in the development and maturation of the central nervous system, and increased activation of TrkB signaling is suggested to promote epilepsy.

CONCLUSION

Although the role of NTRK2 in DRE needs to be elucidated, these results support alternative mechanisms underlying DRE, complementary to the existing hypotheses, that should be evaluated.

Morphological and Advanced Imaging of Epilepsy: Beyond the Basics

The etiology of epilepsy is variable and sometimes multifactorial. Clinical course and response to treatment largely depend on the precise etiology of the seizures. Along with the electroencephalogram (EEG), neuroimaging techniques, in particular, magnetic resonance imaging (MRI), are the most important tools for determining the possible etiology of epilepsy. Over the last few years, there have been many developments in data acquisition and analysis for both morphological and functional neuroimaging of people suffering from this condition. These innovations have increased the detection of underlying structural pathologies, which have till recently been classified as "cryptogenic" epilepsy. Cryptogenic epilepsy is often refractory to anti-epileptic drug treatment. In drug-resistant patients with structural or consistent functional lesions related to the epilepsy syndrome, surgery is the only treatment that can offer a seizure-free outcome. The pre-operative detection of the underlying structural condition increases the odds of successful surgical treatment of pharmacoresistant epilepsy. This article provides a comprehensive overview of neuroimaging techniques in epilepsy, highlighting recent advances and innovations and summarizes frequent etiologies of epilepsy in order to improve the diagnosis and management of patients suffering from seizures, especially young patients and children.

Intravenous immunoglobulin G as adjuvant treatment in drug-resistant childhood epilepsy

BACKGROUND

Epilepsy is the most common neurological disease in childhood; depending on the definition of drug-resistant epilepsy, incidence varies from 10% to 23% in the paediatric population. The objective of this study was to account for the decrease in the frequency and/or monthly duration of epileptic seizures in paediatric patients with drug-resistant epilepsy treated with antiepileptic drugs, before and after adding intravenous immunoglobulin G (iIV IgG).

METHODS

This is an analytic, observational, retrospective case-control study. We studied paediatric patients with drug-resistant epilepsy who were treated with IV IgG at the Centro Médico Nacional 20 de Noviembre, in Mexico City, from 2003 to 2013.

RESULTS

One hundred and sixty seven patients (19.5%) had drug-resistant epilepsy and 44 (5.1%) started adjuvant treatment with IV IgG. The mean age of patients at the beginning of treatment was 6.12 years±5.14); aetiology was structural acquired in 28 patients (73.6%), genetic in 5 (13.1%), immune in 1 (2.6%), and unknown in 4 (10.5%). At 2 months from starting IV IgG, seizure duration had reduced to 66.66%; the frequency of seizures was reduced by 64% at 4 months after starting treatment (P<.001).

CONCLUSIONS

According to the results of this study, intravenous immunoglobulin may be an effective therapy for reducing the frequency and duration of seizures in paediatric patients with drug-resistant epilepsy.