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. After altering the formulations and the method, she became seizure-free.

Improving automated diagnosis of epilepsy from EEGs beyond IEDs.

Objective.Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IED-independent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs.Approach.In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTMs), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India.Main results.In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of three features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps to improve the EEG-level classification of epileptic EEGs with and without IEDs vs. normal EEGs, achieving an AUC of 0.822 (BAC of 77.6%) compared to 0.688 (BAC of 59.6%) for classification only based on the IED rate. Specifically, the addition of IED-independent features improved the BAC by 21% in detecting epileptic EEGs that do not contain IEDs.Significance.These results pave the way towards automated detection of epilepsy. We are one of the first to analyze epileptic EEGs without IEDs, thereby opening up an underexplored option in epilepsy diagnosis.

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. Specifically, we investigate the IED detector based on convolutional neural network (ConvNet) with different input features (temporal, spectral, and wavelet features). We explore different ConvNet architectures and types, including 1D (one-dimensional) ConvNet, 2D (two-dimensional) ConvNet, and noise injection at various layers. We evaluate the EEG classification performance on five independent datasets. The 1D ConvNet with preprocessed full-frequency EEG signal and frequency bands (delta, theta, alpha, beta) with Gaussian additive noise at the output layer achieved the best IED detection results with a false detection rate of 0.23/min at 90% sensitivity. The EEG classification system obtained a mean EEG classification Leave-One-Institution-Out (LOIO) cross-validation (CV) balanced accuracy (BAC) of 78.1% (area under the curve (AUC) of 0.839) and Leave-One-Subject-Out (LOSO) CV BAC of 79.5% (AUC of 0.856). Since the proposed classification system only takes a few seconds to analyze a 30-min routine EEG, it may help in reducing the human effort required for epilepsy diagnosis.

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). These systems are evaluated on channel-, segment-, and EEG-level. The three systems perform prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30 min EEG in 4 s and can be deployed to assist clinicians in interpreting EEGs.

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. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. In this study, we propose a system to classify EEG as epileptic or normal based on multiple modalities extracted from the interictal EEG. The ensemble system consists of three components: a Convolutional Neural Network (CNN)-based IED detector, a Template Matching (TM)-based IED detector, and a spectral feature-based classifier. We evaluate the system on datasets from six centers from the USA, Singapore, and India. The system yields a mean Leave-One-Institution-Out (LOIO) cross-validation (CV) area under curve (AUC) of 0.826 (balanced accuracy (BAC) of 76.1%) and Leave-One-Subject-Out (LOSO) CV AUC of 0.812 (BAC of 74.8%). The LOIO results are found to be similar to the interrater agreement (IRA) reported in the literature for epileptic EEG classification. Moreover, as the proposed system can process routine EEGs in a few seconds, it may aid the clinicians in diagnosing epilepsy efficiently.

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. The input to CNN is a combination of raw EEG and frequency sub-bands, namely delta, theta, alpha and, beta arranged as a vector for one-dimensional (1D) CNN or matrix for two-dimensional (2D) CNN. The proposed method is evaluated on 554 scalp EEGs. The database consists of 18,164 IEDs marked by two neurologists. Five-fold cross-validation was performed to assess the IED detectors. The resulting 1D CNN based IED detector with multiple sub-bands achieved a false positive rate per minute of 0.23 and a precision of 0.79 at 90% sensitivity. Further, the proposed system is evaluated on datasets from three other clinics, and the features extracted from CNN outputs could significantly discriminate (p-values <; 0.05) the EEGs with and without IEDs. We have proposed an optimized method with better performance than the literature that could aid clinicians to diagnose epilepsy expeditiously, and thereby devise proper treatment.

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. METHODS: A drug refractory epilepsy cohort of 139 subjects was selected, based on inclusion and exclusion criteria. All subjects underwent routine history-taking, and a structured interview with QUARAS (in Hindi language) about 3-6 months later when they were admitted for pre-surgical work-up (Video-EEG, MRI, SPECT and PET), by an epilepsy nurse. Seizures were localised and lateralised at the each step separately, in a blinded manner; concordance with the final hypothesis was checked, after the epilepsy-surgery case-conference, and statistical significance of the difference calculated. RESULTS: Auras were reported in significantly more number of patients after administration of QUARAS (p<0.001); there was also higher concordance between the final hypothesis and the localization and lateralization based on QUARAS than an unstructured history (p<0.001). CONCLUSION: Administering a structured questionnaire in the native language of patients by trained personnel leads to better localisation and lateralisation and may help arrive at a hypothesis about the EZ.

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. The ICI a 20 item questionnaire was used to calculate the scores for level (L, question 1-10) and content (C, question 11-20) of consciousness. RESULTS: The patients in mesial temporal group had higher ICI-L scores, p = 0.0129 as compared to the extra temporal group, but no difference was observed in the content of consciousness. The ICI-L and C scores were not different in the mesial temporal and the neocortical temporal group (p = 0.53 and 0.65) respectively. CONCLUSIONS: Patients with mesial temporal epilepsy had a higher level of consciousness than the extra temporal group but there was no difference in the content. Also there was no difference in the level and content of consciousness between mesial and the neocortical temporal group.

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. Although there have been reports of phenotypic differences between MS in Indians and the Western counterparts, recent studies report a reasonable similarity in disease types and characteristics. A few studies on the genetics of MS have been reported, including human leukocyte antigen (HLA) associations and non-major histopathology complex (MHC) disease loci. The current review discusses the pivotal studies of the past, newer observations on MS from India, and the need for a national registry.