Interictal stereo-electroencephalography features below 45 Hz are sufficient for correct localization of the epileptogenic zone and postsurgical outcome prediction
Language English Country United States Media print-electronic
Document type Journal Article
Grant support
22-28784S
Grantová Agentura České Republiky
RVO:68081731
The Czech Academy of Sciences
NU22-08-00278
Ministerstvo Zdravotnictví Ceské Republiky
LM2023053
Ministerstvo Školství, Mládeže a Tělovýchovy
LX22NPO5107
Ministerstvo Školství, Mládeže a Tělovýchovy
PubMed
39180407
DOI
10.1111/epi.18081
Knihovny.cz E-resources
- Keywords
- EEG, epilepsy, high‐frequency oscillations, interictal epileptoform discharges, machine learning,
- MeSH
- Child MeSH
- Adult MeSH
- Electroencephalography * methods MeSH
- Epilepsy surgery physiopathology diagnosis MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Drug Resistant Epilepsy surgery physiopathology diagnosis MeSH
- Stereotaxic Techniques MeSH
- Treatment Outcome MeSH
- Check Tag
- Child MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
OBJECTIVE: Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction. METHODS: In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test. RESULTS: The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution. SIGNIFICANCE: Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.
Department of Biomedical Engineering Duke Pratt School of Engineering Durham North Carolina USA
Department of Neurology Duke University Medical Center Durham North Carolina USA
Institute of Scientific Instruments of the CAS Brno Czech Republic
International Clinical Research Center St Anne's University Hospital Brno Czech Republic
Lyon Neuroscience Research Center INSERM U1028 CNRS UMR5292 Lyon France
Montreal Neurological Hospital McGill University Montreal Quebec Canada
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