NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram
Language English Country United States Media print-electronic
Document type Journal Article, Research Support, Non-U.S. Gov't
Grant support
FDN 143208
CIHR - Canada
Start-up funding of the Montreal Neurological Institute - International
The Molson Neuroengineering fellowship of the Montreal Neurological Institute - International
LTAUSA18056
INTER-ACTION - International
Chercheur-boursier clinicien Junior 2 of the Fonds de Recherche du Québec - Santé - International
FDN 143208
CIHR - Canada
PubMed
31705527
DOI
10.1111/epi.16377
Knihovny.cz E-resources
- Keywords
- connectivity, drug-resistant epilepsy, high-frequency oscillations, machine learning, sleep-wake cycle,
- MeSH
- Action Potentials physiology MeSH
- Wakefulness physiology MeSH
- Adult MeSH
- Electrocorticography methods MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Drug Resistant Epilepsy diagnosis physiopathology MeSH
- Sleep Stages physiology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
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.
Institute of Scientific Instruments The Czech Academy of Sciences Brno Czech Republic
International Clinical Research Center St Anne's University Hospital Brno Czech Republic
Montreal Neurological Institute and Hospital McGill University Montréal Quebec Canada
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