Voltage-based automated detection of postictal generalized electroencephalographic suppression: Algorithm development and validation

Abstract

Objective: Postictal generalized electroencephalographic suppression (PGES) is a pattern of low-voltage scalp electroencephalographic (EEG) activity following termination of generalized seizures. PGES has been associated with both sudden unexplained death in patients with epilepsy and therapeutic efficacy of electroconvulsive therapy (ECT). Automated detection of PGES epochs may aid in reliable quantification of this phenomenon.

Methods: We developed a voltage-based algorithm for detecting PGES. This algorithm applies existing criteria to simulate expert epileptologist readings. Validation relied on postictal EEG recording from patients undergoing ECT (NCT02761330), assessing concordance among the algorithm and four clinical epileptologists.

Results: We observed low-to-moderate concordance among epileptologist ratings of PGES. Despite this, the algorithm displayed high discriminability in comparison to individual epileptologists (C-statistic range: 0.86-0.92). The algorithm displayed high discrimination (C-statistic: 0.91) and substantial peak agreement (Cohen's Kappa: 0.65) in comparison to a consensus of clinical ratings. Interrater agreement between the algorithm and individual epileptologists was on par with that among expert epileptologists.

Conclusions: An automated voltage-based algorithm can be used to detect PGES following ECT, with discriminability nearing that of experts.

Significance: Algorithmic detection may support clinical readings of PGES and improve precision when correlating this marker with clinical outcomes following generalized seizures.

Keywords: Algorithms; Electroconvulsive Therapy (ECT); Major depressive disorder; Postictal generalized EEG suppression; Seizure; Sudden unexplained death in epilepsy.