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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F19%3A00535709" target="_blank" >RIV/68081731:_____/19:00535709 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14110/19:00113177 RIV/00159816:_____/19:00072897

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/epi.16377" target="_blank" >https://onlinelibrary.wiley.com/doi/abs/10.1111/epi.16377</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/epi.16377" target="_blank" >10.1111/epi.16377</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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). 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. 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). 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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). 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. 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). 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/LTAUSA18056" target="_blank" >LTAUSA18056: Lokalizace epileptického ložiska a pokročilá analýza vysokofrekvenčního intrakraniálního EEG</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Epilepsia

ISSN

0013-9580

e-ISSN

—

Svazek periodika

60

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

2404-2415

Kód UT WoS článku

000545973100008

EID výsledku v databázi Scopus

2-s2.0-85074825684