The impact of EEG preprocessing parameters on ultra-low-power seizure detection

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.

Key points

• A major challenge in closed-loop neurostimulation is fast and reliable seizure detection, ideally with minimal energy consumption.

• Many published seizure detection approaches are not ready for application and lack statistical power and comparability.

• The impact of EEG preprocessing parameters was systematically evaluated in 145 focal epilepsy patients.

• The chosen window size and number of channels had the strongest impact on seizure detection performance and energy consumption.

• Our results offer guidance for EEG preprocessing choices to advance the treatment of drug-resistant focal epilepsy.