Voltage-based automated detection of postictal generalized electroencephalographic suppression: Algorithm development and validation
| Autor: | R. Edward Hogan, Alyssa K. Labonte, Ben J.A. Palanca, Emma R. Huels, MohammadMehdi Kafashan, Lawrence N. Eisenman, Eric J. Lenze, Michael S. Avidan, Luigi Maccotta, L. Brian Hickman, Courtney W. Chan, ShiNung Ching, B. Keith Day, Nuri B. Farber |
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| Rok vydání: | 2020 |
| Předmět: |
Epileptologist
medicine.medical_treatment Concordance Electroencephalography 050105 experimental psychology 03 medical and health sciences Epilepsy 0302 clinical medicine Electroconvulsive therapy Physiology (medical) medicine Humans 0501 psychology and cognitive sciences Sudden Unexpected Death in Epilepsy medicine.diagnostic_test business.industry 05 social sciences Reproducibility of Results Sudden unexplained death medicine.disease Sensory Systems Inter-rater reliability Neurology Neurology (clinical) business Algorithm 030217 neurology & neurosurgery Kappa Algorithms |
| Zdroj: | Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 131(12) |
| ISSN: | 1872-8952 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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