Objective Closed-loop neurostimulation is a promising treatment for drug-resistant focal epilepsy. A major challenge is fast and reliable seizure detection via electroencephalography (EEG). While many approaches have been published, they often lack statistical power and practical utility. The use of various EEG preprocessing parameters and performance metrics hampers comparability. Additionally, the critical issue of energy consumption for an application in medical devices is rarely considered. Addressing these points, we present a systematic analysis on the impact of EEG preprocessing parameters on seizure detection performance and energy consumption, using one to four EEG channels. Methods We analyzed in 145 focal epilepsy patients the impact of different sampling rates, window sizes, digital resolutions and number of EEG channels on seizure detection performance and energy consumption. Focusing on practice-relevant metrics, we evaluated seizure detection performance of a state-of-the-art convolutional neural network (CNN) via the Seizure Community Open-Source Research Evaluation (SzCORE) framework. Statistical relevance of parameter changes was assessed using linear mixed-effects models. Energy consumption was analyzed using an ultra-low-power microcontroller. Results Reducing the sampling rate from 256 to 64 Hz led to a decrease in sensitivity and false detections per hour (FD/h; all p values < .001). Longer window sizes reduced the number of FD/h and increased average detection delays between one second and all other sizes (all p values < .001). Lower digital resolutions decreased sensitivity between 16 and 8 bits (p < .001). Using only one EEG channel led to a decrease in sensitivity (p < .001) and FD/h (p = .008) compared to four channels. Energy consumption was especially decreased when reducing the sampling rate and number of EEG channels. Significance This study provides guidance on choosing EEG preprocessing parameters for innovative developments of closed-loop neurostimulation devices to further advance the treatment of drug-resistant focal epilepsy.

PR and CA are employees of Precisis GmbH. ASB has received research support and personal honoraria for lectures and advice from Precisis GmbH. The remaining authors have no conflicts of interest.

This work was supported by the German federal state of Baden-Wuerttemberg, through the Invest BW Project "Brain-MEP" (BW1_1276/03).

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The Ethics Committees of the three hospitals involved in the development of the EPILEPSIAE database (Ethik-Kommission der Albert-Ludwigs-Universitaet, Freiburg; Comite consultatif sur le traitement de l'information en matiere de recherche dans le domaine de la sante, Hospital Universitario Pitie-Salpetriere; and Ethics Committee of the Centro Hospitalar e Universitario de Coimbra) gave ethical approval for this work.

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