ArticleEpilepsia2025
The impact of EEG preprocessing parameters on ultra-low-power seizure detection.
Article in Epilepsia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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9 authors.
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Abstract
objectiveClosed-loop neurostimulation is a promising treatment for drug-resistant focal epilepsy. A major challenge is fast and reliable seizure detection via electroencephalography (EEG). Although 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 of the impact of EEG preprocessing parameters on seizure detection performance and energy consumption, using one to four EEG channels.
methodsWe analyzed in 145 patients with focal epilepsy the impact of different sampling rates, window sizes, digital resolutions, and number of EEG channels on seizure detection performance and energy consumption. Focusing on clinically relevant, event-based 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.
resultsReducing the sampling rate from 256 to 64 Hz decreased sensitivity (p = .015) and false detections per hour (FD/h; p = .002). Larger windows reduced FD/h between 1 s and all other sizes (all p < .001). Lower digital resolution decreased sensitivity between 16 and 8 bits (p = .007). Using one EEG channel instead of four decreased sensitivity (p < .001) and increased detection delay (p = .020), but also reduced FD/h (p = .005). Reducing the sampling rate to 64 Hz lowered CNN energy consumption from 49.15 to 17.26 μJ/s, and using one channel reduced it from 79.04 to 31.63 μJ/s. 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.
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