Fingerprinting the Epileptogenic Zone in RAVE

INTRODUCTION: The treatment of drug-resistant epilepsy remains a significant challenge. Surgical resection of the epileptogenic zone (EZ) often offers the highest chance of seizure freedom. Grinenko et al. [Brain. 2018; 141(1):117–131] demonstrated that intracranial EEG (iEEG) data analysis can be used to identify a time-frequency pattern or “fingerprint” to help identify the EZ. Complex algorithmic techniques, lack of standardization, and inaccessibility pose a hurdle to surgical programs that may consider fingerprinting as a tool to aid resection. R Analysis and Visualization of Intracranial EEG (RAVE) is a powerful, free, open-source, NIH-funded software designed to analyze iEEG data through a web browser on an internet-connected device. Here we present an accessible implementation of a fingerprinting algorithm with RAVE. METHODS: Our fingerprint module consists of three steps, following closely the work of Grinenko et al. First, iEEG seizure data is pre-processed with Morlet wavelet transform and bipolar normalization. Second, each electrode's time-frequency map is run through an image processing algorithm to extract the EZ fingerprint biomarkers: (1) pre-ictal spikes, (2) multiband fast activity, and (3) low-frequency suppression. Each electrode is ranked by biomarker and combined “fingerprint” score based on work by Woolfe et al. [J Neurosci Methods. 2019; 325:108347]. Data is then displayed via time-frequency plot for each electrode and the fingerprint score is projected across the brain with a 3D brain map viewer, RESULTS: Our fingerprint module in RAVE significantly streamlines EZ isolation. Time-consuming calculations are built-in, allowing users with no coding, image processing, or statistical experience to explore and share EZ fingerprinting data. CONCLUSIONS: We incorporated an algorithm to detect the epileptogenic zone fingerprint into RAVE, allowing free distribution and use of this research algorithm to surgical epilepsy programs.