Multiple ASM Administration via Percutaneous Feeding Tube as a Cause of Pseudo-Refractoriness Leading to Recurrent Seizure Clusters.

It is important to identify true refractoriness of seizures, before escalation of anti-seizure medications, to avoid side effects of medications. Bioavailability of medications changes with the formulations used and changes significantly with the route of administration. Both of these were significantly impacted in a lady who was being fed via percutaneous endoscopic gastrostomy (PEG) feeds and deemed refractory to medications.

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free versus IED EEGs. In this study, we evaluate features that may provide reliable IED detection and EEG classification.

Multi-Center Validation Study of Automated Classification of Pathological Slowing in Adult Scalp Electroencephalograms Via Frequency Features.

Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detection System (TDS), Shallow Learning-based Detection System (SLDS), and Deep Learning-based Detection System (DLDS).

Automated Adult Epilepsy Diagnostic Tool Based on Interictal Scalp Electroencephalogram Characteristics: A Six-Center Study.

The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan.

Deep Learning for Interictal Epileptiform Spike Detection from scalp EEG frequency sub bands.

Epilepsy diagnosis through visual examination of interictal epileptiform discharges (IEDs) in scalp electroencephalogram (EEG) signals is a challenging problem. Deep learning methods can be an automated way to perform this task. In this work, we present a new approach based on convolutional neural network (CNN) to detect IEDs from EEGs automatically.

Evaluation of Ictal Consciousness in Temporal and Extra Temporal Epilepsy: Observations from a Tertiary Care Hospital in India.

Background And Purpose: Differences in consciousness during seizures depend on the location of the seizure onset.

Methods: The present study evaluates ictal consciousness using the ictal consciousness inventory (ICI) in drug refractory mesial temporal (MTLE), neocortical temporal (NTLE) and extra temporal epilepsy (ETLE). This was a cross sectional cohort study with 45 patients with mesial temporal epilepsy, 47 with extra temporal and 11 patients with neocortical temporal epilepsy.

Utility of a questionnaire tool (QUARAS) for localizing and lateralizing seizures in the epilepsy monitoring unit (EMU).

Objectives: An accurate description of the seizure semiology improves the recognition of the ictal onset zone and helps in hypothesizing the possible epileptogenic zone (EZ). Semiology based on a reliable description of seizures may be as good as investigative modalities, as has been shown by numerous studies. The main objective of this study was to apply a questionnaire-tool for auras and semiology (QUARAS) in refractory epilepsy cohort and compare its yield to that of standard history-taking.

Epidemiology and genetic aspects of multiple sclerosis in India.

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system with a complex pathophysiology. Considered a rare disease in India in the past, studies over time suggest an increase in subjects with MS in India, although the observations are limited by the lack of formally conducted epidemiological studies and the absence of a nationwide registry. The current World Health Organization (WHO) Multiple Sclerosis International Federation (MSIF) "Atlas of MS" 2013 estimates a prevalence rate of 5-20 per 100,000, which also seems an underestimate.