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.

Analytical Neurophysiology Lab Montreal Neurological Institute and Hospital McGill University Quebec Canada

Behavioral and Social Neuroscience Research Group CEITEC Central European Institute of Technology Masaryk University Brno Czech Republic

Center for Sleep Medicine and Respiratory Diseases Lyon University Hospital Lyon 1 University Lyon France

Department of Biomedical Engineering Duke Pratt School of Engineering Durham North Carolina USA

Department of Neurology Duke University Medical Center Durham North Carolina USA

Department of Neurology Faculty of Medicine Brno Epilepsy Center St Anne's University Hospital Member of ERN EpiCARE Masaryk University Brno Czech Republic

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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