Seizure clusters in drug-resistant focal epilepsy.

We investigated clinical factors associated with seizure clustering in patients with drug-resistant focal epilepsy and any association between seizure clustering and outcome after surgery. We performed a retrospective study including patients with a diagnosis of drug-resistant focal epilepsy who underwent epilepsy surgery. Patients were prospectively registered in a database from 1986 until 2015. Seizure cluster was defined as two or more seizures occurring within 2 days. Potential risk factors for seizure clustering were assessed. To investigate any potential association between seizure clusters and seizure outcome after surgery, time to event analysis was used to produce a Kaplan-Meier estimate of seizure recurrence. We studied 764 patients. Seizure clusters were reported in 23.6% of patients with temporal lobe epilepsy (TLE) and 16.9% of extratemporal patients (p = 0.2). We could not identify any significant clinical factors associated with seizure clustering. Among patients with TLE, those who had history of seizure clusters fared better after surgery (p \u3c 0.01). We found that seizure clusters relate to prognosis after temporal lobe surgery in drug-resistant TLE. These data may provide added value for surgical prognostication when combined with other data types. A better understanding of the neurobiology underlying seizure clusters is needed

Timing matters: impact of anticonvulsant drug treatment and spikes on seizure risk in benign epilepsy with centrotemporal spikes

OBJECTIVE: Benign epilepsy with centrotemporal spikes (BECTS) is a common, self-limited epilepsy syndrome affecting school-age children. Classic interictal epileptiform discharges (IEDs) confirm diagnosis, and BECTS is presumed to be pharmacoresponsive. As seizure risk decreases in time with this disease, we hypothesize that the impact of IEDs and anticonvulsive drug (ACD) treatment on the risk of subsequent seizure will differ based on disease duration. METHODS: We calculate subsequent seizure risk following diagnosis in a large retrospective cohort of children with BECTS (n = 130), evaluating the impact of IEDs and ACD treatment in the first, second, third, and fourth years of disease. We use a Kaplan-Meier survival analysis and logistic regression models. Patients were censored if they were lost to follow-up or if they changed group status. RESULTS: Two-thirds of children had a subsequent seizure within 2 years of diagnosis. The majority of children had a subsequent seizure within 3 years despite treatment. The presence of IEDs on electroencephalography (EEG) did not impact subsequent seizure risk early in the disease. By the fourth year of disease, all children without IEDs remained seizure free, whereas one-third of children with IEDs at this stage had a subsequent seizure. Conversely, ACD treatment corresponded with lower risk of seizure early in the disease but did not impact seizure risk in later years. SIGNIFICANCE: In this cohort, the majority of children with BECTS had a subsequent seizure despite treatment. In addition, ACD treatment and IEDs predicted seizure risk at specific points of disease duration. Future prospective studies are needed to validate these exploratory findings.Published versio

Patient historical risk factors associated with seizure outcome after surgery for drug-resistant nonlesional temporal lobe epilepsy.

OBJECTIVE: To investigate the possible influence of risk factors on seizure outcome after surgery for drug-resistant nonlesional temporal lobe epilepsy (TLE). METHODS: This retrospective study recruited patients with drug-resistant nonlesional TLE who underwent epilepsy surgery at Jefferson Comprehensive Epilepsy Center and were followed for a minimum of one year. Patients had been prospectively registered in a database from 1991 through 2014. Postsurgical outcome was classified into two groups; seizure free or relapsed. The possible risk factors influencing long-term seizure outcome after surgery were investigated. RESULTS: Ninety-five patients (42 males and 53 females) were studied. Fifty-four (56.8%) patients were seizure free. Only a history of febrile seizure in childhood affected the risk of post-operative seizure recurrence (odds ratio 0.22; 95% CI: 0.06-0.83; p = 0.02). Gender, race, family history of epilepsy, history of status epilepticus, duration of disease before surgery, aura symptoms, intelligence quotient, and seizure type or frequency were not predictors of outcome. CONCLUSION: Many patients with drug-resistant nonlesional TLE responded favorably to surgery. The only factor predictive of seizure outcome after surgery was a history of febrile seizure in childhood. It is critical to distinguish among different types of TLE when assessing outcome after surgery

A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients

Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction

Performance metrics for characterization of a seizure detection algorithm for offline and online use

Purpose: To select appropriate previously reported performance metrics to evaluate a new seizure detection algorithm for offline and online analysis, and thus quantify any performance variation between these metrics. Methods: Traditional offline algorithms mark out any EEG section (epoch) of a seizure (event), so that neurologists only analyze the detected and adjacent sections. Thus, offline algorithms could be evaluated using number of correctly detected events, or event-based sensitivity (SEVENT), and epoch-based specificity (percentage of incorrectly detected background epochs). In contrast, online seizure detection (especially, data selection) algorithms select for transmission only the detected EEG sections and hence need to detect the entire duration of a seizure. Thus, online algorithms could be evaluated using percentage of correctly detected seizure duration, or epoch-based sensitivity (SEPOCH), and epoch-based specificity. Here, a new seizure detection algorithm is evaluated using the selected performance metrics for epoch duration ranging from 1s to 60s. Results: For 1s epochs, the area under the event-based sensitivity-specificity curve was 0.95 whilst SEPOCH achieves 0.81. This difference is not surprising, as intuitively, detecting any epoch within a seizure is easier than detecting every epoch - especially as seizures evolve over time. For longer epochs of 30s or 60s, SEVENT falls to 0.84 and 0.82 respectively and SEPOCH reduces to 0.76. Here, decreased SEVENT shows that fewer seizures are detected, possibly due to easy-to-detect short seizure sections being masked by surrounding EEG. However, detecting one long epoch constitutes a larger percentage of a seizure than a shorter one and thus SEPOCH does not decrease proportionately. Conclusions: Traditional offline and online seizure detection algorithms require different metrics to effectively evaluate their performance for their respective applications. Using such metrics, it has been shown that a decrease in performance may be expected when an offline seizure detection algorithm (especially with short epoch duration) is used for online analysis.Accepted versio

SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease. This task is challenging due to factors such as low signal-to-noise ratios, signal artefacts, high variance in seizure semiology among epileptic patients, and limited availability of clinical data. To overcome these challenges, in this paper, we present SeizureNet, a deep learning framework which learns multi-spectral feature embeddings using an ensemble architecture for cross-patient seizure type classification. We used the recently released TUH EEG Seizure Corpus (V1.4.0 and V1.5.2) to evaluate the performance of SeizureNet. Experiments show that SeizureNet can reach a weighted F1 score of up to 0.94 for seizure-wise cross validation and 0.59 for patient-wise cross validation for scalp EEG based multi-class seizure type classification. We also show that the high-level feature embeddings learnt by SeizureNet considerably improve the accuracy of smaller networks through knowledge distillation for applications with low-memory constraints