## 4 Conclusions  
We tested the performance of popular outlier detection algorithms in labelling epileptic seizures in human EEGs. We showed that distance-based methods may outperform SVMs and random forests if the distance calculation and feature space are properly defined. Thus, we obtained the best score from the LNND trained on four PCAs explaining 90% of the variance in the spectral power in the 1-30 Hz frequency range. Finally, we tested if the distance between the classes (separability) in feature space, a core of the distance-based algorithm, predicts its performance. We observed that PCA provided better separability regardless of the frequency band. Therefore, we suggest that estimating the distance between the classes may predict the algorithm's performance providing opportunities for feature selection.  
Conceptualization, O.E.K., D.A.A. and A.E.H.; data curation, O.E.K., V.V.G. and N.U.; formal analysis, V.A.M.; funding acquisition, D.A.A. and A.E.H.; investigation, M.S.K.; methodology, O.E.K., S.A.K., V.A.M. and A.E.H.; project administration, O.E.K. and D.A.A.; resources, O.E.K., N.U. and D.A.A.; software, M.S.K. and V.V.G.; supervision, A.E.H.; validation, V.A.M., V.V.G., S.A.K., M.S.K. and A.E.H.; visualization, S.A.K. and M.S.K.; writing--original draft, V.A.M. and V.V.G.; writing--review and editing, O.E.K., S.A.K. and A.E.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Academic Leadership Program \"PRIORITY-200\" of Immanuel Kant Baltic Federal University of Ministry of Science and Education of Russian Federation. AEH thanks the Leading Scientific School Support Program (grant NSH-589.2022.1.2). SAK thanks the Doctor Support Program (grant MD-590.2022.1.2).  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Immanuel Kant Baltic Federal University (protocol code 7/2022 of 27 June 2022).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data that support the findings of this study are available from the National Medical and Surgical Center named after [PERSON] of Russian Healthcare Ministry but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the National Medical and Surgical Center named after [PERSON] of Russian Healthcare Ministry.  
The authors declare no conflicts of interest. The funders and organizations had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} EEG & Electroencephalogram \\ ML & Machine learning \\ DL & Deep learning \\ RNN & Recurrent neural networks \\ LSTM & Long short-term memory \\ DSS & Decision support system \\ SVM & Support vector machine \\ PC & Principle component \\ ICA & Independent component analysis \\ CWT & Continuous wavelet transform \\ WP & Wavelet power \\ PCA & Principle component analysis \\ OCSVM & One-class support vector machine \\ kNN & k-Nearest neighbours \\ LNND & Local nearest neighbours distance \\ LOF & Local outlier factor \\ IF & Isolation forest \\ TP & True positive \\ FP & False positive \\ FN & False negative \\ rm ANOVA & Repeated measures analysis of variances \\ sCSP & Sparse common spatial pattern \\ FSST & Fourier transform-based synchrosqueezing transform \\ \end{tabular}