Machine Learning‐Guided Co‐Design of Compact EEG Acquisition for Epileptic Seizure Detection

This is the authors' abstract. We don't add key points for this paper.

Electroencephalography (EEG)‐based seizure detection is increasingly being considered for wearable neurological monitoring, but conventional approaches often rely on dense electrode configurations and high‐dimensional feature representations. This letter presents a machine learning‐guided co‐design framework in which feature relevance informs EEG acquisition design. From 18 bipolar derivations of the CHB‐MIT database, 270 wavelet descriptors were extracted. The available reduced inputs comprise 10 features from nine derivations and a separate four‐feature configuration comprising four bipolar derivations and eight scalp electrodes. In 75/25 sample‐level evaluations, random forest achieved 93.75%, 91.67% and 93.75% accuracy for the full, 10‐feature, and four‐feature inputs, respectively. These exploratory results demonstrate a traceable link between feature relevance and acquisition‐design decisions, supporting the proposed feature‐to‐derivation mapping as a candidate strategy for compact EEG acquisition while motivating subsequent patient‐independent and wearable validation.