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

L. Brian Hickman; R. Edward Hogan; Alyssa K. Labonte; Mohammad Mehdi Kafashan; Courtney W. Chan; Emma R. Huels; Shi Nung Ching; Eric J. Lenze; Luigi Maccotta; Lawrence N. Eisenman; B. Keith Day; Nuri B. Farber; Michael S. Avidan; Ben Julian A. Palanca

doi:10.1016/j.clinph.2020.08.015

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

L. Brian Hickman, R. Edward Hogan, Alyssa K. Labonte, Mohammad Mehdi Kafashan, Courtney W. Chan, Emma R. Huels, Shi Nung Ching, Eric J. Lenze, Luigi Maccotta, Lawrence N. Eisenman, B. Keith Day, Nuri B. Farber, Michael S. Avidan, Ben Julian A. Palanca

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

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.

Original language	English
Pages (from-to)	2817-2825
Number of pages	9
Journal	Clinical Neurophysiology
Volume	131
Issue number	12
DOIs	https://doi.org/10.1016/j.clinph.2020.08.015
State	Published - Dec 2020

Keywords

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

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10.1016/j.clinph.2020.08.015

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Brian Hickman, L., Edward Hogan, R., Labonte, A. K., Kafashan, M. M., Chan, C. W., Huels, E. R., Ching, S. N., Lenze, E. J., Maccotta, L., Eisenman, L. N., Keith Day, B., Farber, N. B., Avidan, M. S., & Palanca, B. J. A. (2020). Voltage-based automated detection of postictal generalized electroencephalographic suppression: Algorithm development and validation. Clinical Neurophysiology, 131(12), 2817-2825. https://doi.org/10.1016/j.clinph.2020.08.015

@article{18558e130cae4aa68900827bc73f2ccc,

title = "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",

author = "{Brian Hickman}, L. and {Edward Hogan}, R. and Labonte, {Alyssa K.} and Kafashan, {Mohammad Mehdi} and Chan, {Courtney W.} and Huels, {Emma R.} and Ching, {Shi Nung} and Lenze, {Eric J.} and Luigi Maccotta and Eisenman, {Lawrence N.} and {Keith Day}, B. and Farber, {Nuri B.} and Avidan, {Michael S.} and Palanca, {Ben Julian A.}",

note = "Publisher Copyright: {\textcopyright} 2020",

year = "2020",

month = dec,

doi = "10.1016/j.clinph.2020.08.015",

language = "English",

volume = "131",

pages = "2817--2825",

journal = "Clinical Neurophysiology",

issn = "1388-2457",

number = "12",

}

Brian Hickman, L, Edward Hogan, R, Labonte, AK, Kafashan, MM, Chan, CW, Huels, ER, Ching, SN , Lenze, EJ , Maccotta, L , Eisenman, LN , Keith Day, B , Farber, NB , Avidan, MS & Palanca, BJA 2020, 'Voltage-based automated detection of postictal generalized electroencephalographic suppression: Algorithm development and validation', Clinical Neurophysiology, vol. 131, no. 12, pp. 2817-2825. https://doi.org/10.1016/j.clinph.2020.08.015

TY - JOUR

T1 - Voltage-based automated detection of postictal generalized electroencephalographic suppression

T2 - Algorithm development and validation

AU - Brian Hickman, L.

AU - Edward Hogan, R.

AU - Labonte, Alyssa K.

AU - Kafashan, Mohammad Mehdi

AU - Chan, Courtney W.

AU - Huels, Emma R.

AU - Ching, Shi Nung

AU - Lenze, Eric J.

AU - Maccotta, Luigi

AU - Eisenman, Lawrence N.

AU - Keith Day, B.

AU - Farber, Nuri B.

AU - Avidan, Michael S.

AU - Palanca, Ben Julian A.

PY - 2020/12

Y1 - 2020/12

N2 - 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.

AB - 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.

KW - Algorithms

KW - Electroconvulsive Therapy (ECT)

KW - Major depressive disorder

KW - Postictal generalized EEG suppression

KW - Seizure

KW - Sudden unexplained death in epilepsy

UR - http://www.scopus.com/inward/record.url?scp=85094617633&partnerID=8YFLogxK

U2 - 10.1016/j.clinph.2020.08.015

DO - 10.1016/j.clinph.2020.08.015

M3 - Article

C2 - 33137572

AN - SCOPUS:85094617633

SN - 1388-2457

VL - 131

SP - 2817

EP - 2825

JO - Clinical Neurophysiology

JF - Clinical Neurophysiology

IS - 12

ER -

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

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