Objective: High frequency oscillations (HFOs) are a promising biomarker of epileptogenicity, and automated algorithms are critical tools for their detection. However, it is not uncommon for a previously validated algorithm to work poorly when applied to a new data set. There is no consensus on whether (or how) parameters should be optimized. Here we evaluate the impact of parameter selection on seizure onset zone (SOZ) localization using automatically detected HFOs. Methods: We detected HFOs in intracranial EEG from twenty medically refractory epilepsy patients with seizure free surgical outcomes using an automated algorithm. For each patient, we assessed classification accuracy of channels inside/outside the SOZ using a wide range of detection parameters and identified the parameters associated with maximum classification accuracy. Results: Only three of twenty patients achieved maximal localization accuracy using conventional HFO detection parameters, and optimal parameter ranges varied significantly across patients. The use of individualized optimal parameters led to substantial improvements in localization accuracy, particularly in reducing HFO rates in non-SOZ channels. Conclusion: Optimal HFO detection parameters are patient-specific and often differ from conventional parameters. The use of optimal parameters significantly improves SOZ localization. Significance: Individual variability should be considered when implementing automatic HFO detection, and novel methods for patient-specific optimization are needed.
bioRxiv Subject Collection: Neuroscience