Abstract
OBJECTIVE: The goal of this study was to determine the consistency of human reviewer spike detection and then develop a computer algorithm to make the intracranial spike detection process more objective and reliable. METHODS: Three human reviewers marked interictal spikes on samples of intracranial EEGs from 10 patients. The sensitivity, precision and agreement in channel ranking by activity were calculated between reviewers. A computer algorithm was developed to parallel the way human reviewers detect spikes by first identifying all potential spikes on each channel using frequency filtering and then block scaling all channels at the same time in order to exclude potential spikes that fall below an amplitude and slope threshold. Its performance was compared to the human reviewers on the same set of patients. RESULTS: Human reviewers showed surprisingly poor inter-reviewer agreement, but did broadly agree on the ranking of channels for spike activity. The computer algorithm performed as well as the human reviewers and did especially well at ranking channels from highest to lowest spike frequency. CONCLUSIONS: Our algorithm showed good agreement with the different human reviewers, even though they demonstrated different criteria for what constitutes a 'spike' and performed especially well at the clinically important task of ranking channels by spike activity. SIGNIFICANCE: An automated, objective method to detect interictal spikes on intracranial recordings will improve both research and the surgical management of epilepsy patients.