Absence-to-bilateral-tonic-clonic seizure: A generalized seizure type

A generalized seizure type: Myoclonic-to-tonic seizure

BACKGROUND AND PURPOSE

To test the hypothesis that myoclonic seizures can evolve to tonic seizures, we documented the electroclinical features of this under-recognized seizure type.

METHODS

We observed a distinct seizure pattern starting with myoclonus without returning to an interictal state, which subsequently evolved into generalized tonic seizures. The detailed symptomatic and electroencephalographic characteristics of this seizure were extracted, and the clinical manifestations, drug curative responses in patients with this seizure were reviewed and analyzed.

RESULTS

The onset of all seizures was characterized by a preceding period of myoclonus and bursts of generalized spike or poly-spike slow wave discharges with high amplitude. This was closely followed by the occurrence of tonic seizures, which were distinguished by bursts of generalized fast activity at 10 Hz or higher frequency. This under-recognized seizure type has been designated as myoclonic-to-tonic (MT) seizure. The number of patients identified with MT seizures in this study was 34. The prevalence rate of MT seizures was found to be higher in males. While MT seizures typically included a tonic component, it should be noted that some patients experiencing this seizure type never presented with isolated tonic seizures. Generalized Epilepsy not further defined (GE) accounted for approximately one-third of the diagnosed cases, followed by Lennox-Gastaut syndrome and Epilepsy with Myoclonic-Atonic seizures. In comparison to other types of epilepsy, GE with MT seizures demonstrated a more favorable prognosis.

CONCLUSIONS

The classification of myoclonic-to-tonic seizure represents a novel approach in comprehending the ictogenesis of generalized seizures and can provide valuable assistance to clinicians in epilepsy diagnosis.

Seizure semiology: ILAE glossary of terms and their significance

This educational topical review and Task Force report aims to address learning objectives of the International League Against Epilepsy (ILAE) curriculum. We sought to extract detailed features involving semiology from video recordings and interpret semiological signs and symptoms that reflect the likely localization for focal seizures in patients with epilepsy. This glossary was developed by a working group of the ILAE Commission on Diagnostic Methods incorporating the EEG Task Force. This paper identifies commonly used terms to describe seizure semiology, provides definitions, signs and symptoms, and summarizes their clinical value in localizing and lateralizing focal seizures based on consensus in the published literature. Video-EEG examples are included to illustrate important features of semiology in patients with epilepsy.

Features importance in seizure classification using scalp EEG reduced to single timeseries

Seizure detection and seizure-type classification are best performed using intra-cranial or full-scalp electroencephalogram (EEG). In embedded wearable systems however, recordings from only a few electrodes are available, reducing the spatial resolution of the signals to a handful of timeseries at most. Taking this constraint into account, we tested the performance of multiple classifiers using a subset of the EEG recordings by selecting a single trace from the montage or performing a dimensionality reduction over each hemispherical space. Our results support that Random Forest (RF) classifiers lead most efficient and stable classification performances over Support Vector Machines (SVM). Interestingly, tracking the feature importances using permutation tests reveals that classical EEG spectrum power bands display different rankings across the classifiers: low frequencies (delta, theta) are most important for SVMs while higher frequencies (alpha, gamma) are more relevant for RF and Decision Trees. We reach up to 94.3% ∓ 5.3% accuracy in classifying absence from tonic-clonic seizures using state-of-art sampling methods for unbalanced datasets and leave-patients-out 3-fold cross-validation policy.