NREM sleep is the state of vigilance that best identifies the
epileptogenic zone in the interictal electroencephalogram

J 2019

NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram

KLIMES, Petr, Jan CIMBÁLNÍK, Milan BRÁZDIL, Jeffery HALL, Francois DUBEAU et. al.

Basic information

Original name

NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram

Authors

KLIMES, Petr (203 Czech Republic), Jan CIMBÁLNÍK (203 Czech Republic), Milan BRÁZDIL (203 Czech Republic, belonging to the institution), Jeffery HALL (124 Canada), Francois DUBEAU (124 Canada), Jean GOTMAN (124 Canada) and Brigit FRAUSCHER (124 Canada, guarantor)

Edition

Epilepsia, Blackwell Science, 2019, 0013-9580

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30210 Clinical neurology

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 6.040

RIV identification code

RIV/00216224:14110/19:00113177

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1111/epi.16377

UT WoS

000545973100008

Keywords in English

connectivity; drug-resistant epilepsy; high-frequency oscillations; machine learning; sleep-wake cycle

Abstract

V originále

OBJECTIVE: Interictal epileptiform anomalies such as epileptiform discharges or high-frequency oscillations show marked variations across the sleep-wake cycle. This study investigates which state of vigilance is the best to localize the epileptogenic zone (EZ) in interictal intracranial electroencephalography (EEG). METHODS: Thirty patients with drug-resistant epilepsy undergoing stereo-EEG (SEEG)/sleep recording and subsequent open surgery were included; 13 patients (43.3%) had good surgical outcome (Engel class I). Sleep was scored following standard criteria. Multiple features based on the interictal EEG (interictal epileptiform discharges, high-frequency oscillations, univariate and bivariate features) were used to train a support vector machine (SVM) model to classify SEEG contacts placed in the EZ. The performance of the algorithm was evaluated by the mean area under the receiver-operating characteristic (ROC) curves (AUCs) and positive predictive values (PPVs) across 10-minute sections of wake, non-rapid eye movement sleep (NREM) stages N2 and N3, REM sleep, and their combination. RESULTS: Highest AUCs were achieved in NREM sleep stages N2 and N3 compared to wakefulness and REM (P < .01). There was no improvement when using a combination of all four states (P > .05); the best performing features in the combined state were selected from NREM sleep. There were differences between good (Engel I) and poor (Engel II-IV) outcomes in PPV (P < .05) and AUC (P < .01) across all states. The SVM multifeature approach outperformed spikes and high-frequency oscillations (P < .01) and resulted in results similar to those of the seizure-onset zone (SOZ; P > .05). SIGNIFICANCE: Sleep improves the localization of the EZ with best identification obtained in NREM sleep stages N2 and N3. Results based on the multifeature classification in 10 minutes of NREM sleep were not different from the results achieved by the SOZ based on 12.7 days of seizure monitoring. This finding might ultimately result in a more time-efficient intracranial presurgical investigation of focal epilepsy.

Citovat

KLIMES, Petr, Jan CIMBÁLNÍK, Milan BRÁZDIL, Jeffery HALL, Francois DUBEAU, Jean GOTMAN and Brigit FRAUSCHER. NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram. Epilepsia. Blackwell Science, 2019, vol. 60, No 12, p. 2404-2415. ISSN 0013-9580. Available from: https://dx.doi.org/10.1111/epi.16377.

@article{1630897,
   author = {Klimes, Petr and Cimbálník, Jan and Brázdil, Milan and Hall, Jeffery and Dubeau, Francois and Gotman, Jean and Frauscher, Brigit},
   article_number = {12},
   doi = {http://dx.doi.org/10.1111/epi.16377},
   keywords = {connectivity; drug-resistant epilepsy; high-frequency oscillations; machine learning; sleep-wake cycle},
   language = {eng},
   issn = {0013-9580},
   journal = {Epilepsia},
   title = {NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram},
   url = {https://europepmc.org/article/med/31705527},
   volume = {60},
   year = {2019}
}

TY  - JOUR
ID  - 1630897
AU  - Klimes, Petr - Cimbálník, Jan - Brázdil, Milan - Hall, Jeffery - Dubeau, Francois - Gotman, Jean - Frauscher, Brigit
PY  - 2019
TI  - NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram
JF  - Epilepsia
VL  - 60
IS  - 12
SP  - 2404-2415
EP  - 2404-2415
PB  - Blackwell Science
SN  - 00139580
KW  - connectivity
KW  - drug-resistant epilepsy
KW  - high-frequency oscillations
KW  - machine learning
KW  - sleep-wake cycle
UR  - https://europepmc.org/article/med/31705527
L2  - https://europepmc.org/article/med/31705527
N2  - OBJECTIVE: Interictal epileptiform anomalies such as epileptiform discharges or high-frequency oscillations show marked variations across the sleep-wake cycle. This study investigates which state of vigilance is the best to localize the epileptogenic zone (EZ) in interictal intracranial electroencephalography (EEG). METHODS: Thirty patients with drug-resistant epilepsy undergoing stereo-EEG (SEEG)/sleep recording and subsequent open surgery were included; 13 patients (43.3%) had good surgical outcome (Engel class I). Sleep was scored following standard criteria. Multiple features based on the interictal EEG (interictal epileptiform discharges, high-frequency oscillations, univariate and bivariate features) were used to train a support vector machine (SVM) model to classify SEEG contacts placed in the EZ. The performance of the algorithm was evaluated by the mean area under the receiver-operating characteristic (ROC) curves (AUCs) and positive predictive values (PPVs) across 10-minute sections of wake, non-rapid eye movement sleep (NREM) stages N2 and N3, REM sleep, and their combination. RESULTS: Highest AUCs were achieved in NREM sleep stages N2 and N3 compared to wakefulness and REM (P < .01). There was no improvement when using a combination of all four states (P > .05); the best performing features in the combined state were selected from NREM sleep. There were differences between good (Engel I) and poor (Engel II-IV) outcomes in PPV (P < .05) and AUC (P < .01) across all states. The SVM multifeature approach outperformed spikes and high-frequency oscillations (P < .01) and resulted in results similar to those of the seizure-onset zone (SOZ; P > .05). SIGNIFICANCE: Sleep improves the localization of the EZ with best identification obtained in NREM sleep stages N2 and N3. Results based on the multifeature classification in 10 minutes of NREM sleep were not different from the results achieved by the SOZ based on 12.7 days of seizure monitoring. This finding might ultimately result in a more time-efficient intracranial presurgical investigation of focal epilepsy.
ER  -

KLIMES, Petr, Jan CIMBÁLNÍK, Milan BRÁZDIL, Jeffery HALL, Francois DUBEAU, Jean GOTMAN and Brigit FRAUSCHER. NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram. \textit{Epilepsia}. Blackwell Science, 2019, vol.~60, No~12, p.~2404-2415. ISSN~0013-9580. Available from: https://dx.doi.org/10.1111/epi.16377.

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