Spurious Spike Elimination Using Sparse Signal Processing Improves Seizure Onset Zone Delineation in Brief Intraoperative iEEG Recordings

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Interictal epileptiform spikes and high frequency oscillations (HFOs) have proven to be promising neuro biomarkers for seizure onset zone (SOZ) identification in drug-resistant epilepsy. This study presents a sparse signal processing based denoising model for epileptiform spikes. The model was trained on expert-labeled events to remove artifacts and spurious detections from the initial candidate spike pool captured using an amplitude threshold-based detector. We hypothesize that interictal spikes exhibit a sparse representation with a limited number of atoms in analytical dictionary, whereas artifacts, due to their unstructured waveshape, lack such a representation. We employed orthogonal matching pursuit (OMP) with a Gabor analytical redundant dictionary for event representation and Random Forrest (RF) classifier for event classification. The optimal model was further evaluated over the intracranial EEG (iEEG) from 29 subjects during intraoperative monitoring (IOM). Denoising method significantly improved the SOZ delineation of spatial distribution of spike (36% to 52% in IOM). Additionally, we included HFO analysis results to provide further comparison with spikes yielding an average denoised SOZ ratio of 69% in IOM. These advancements could enhance clinical decision-making by offering reliable initial assessments during a brief intraoperative recording.

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