A novel scheme for the validation of an automated classification method for epileptic spikes by comparison with multiple observers


Objective To validate the application of an automated neuronal spike classification algorithm, Wave_clus (WC), on interictal epileptiform discharges (IED) obtained from human intracranial EEG (icEEG) data. Method Five 10-min segments of icEEG recorded in 5 patients were used. WC and three expert EEG reviewers independently classified one hundred IED events into IED classes or non-IEDs. First, we determined whether WC-human agreement variability falls within inter-reviewer agreement variability by calculating the variation of information for each classifier pair and quantifying the overlap between all WC-reviewer and all reviewer-reviewer pairs. Second, we compared WC and EEG reviewers' spike identification and individual spike class labels visually and quantitatively. Results The overlap between all WC-human pairs and all human pairs was textgreater80% for 3/5 patients and textgreater58% for the other 2 patients demonstrating WC falling within inter-human variation. The average sensitivity of spike marking for WC was 91% and textgreater87% for all three EEG reviewers. Finally, there was a strong visual and quantitative similarity between WC and EEG reviewers. Conclusions WC performance is indistinguishable to that of EEG reviewers' suggesting it could be a valid clinical tool for the assessment of IEDs. Significance WC can be used to provide quantitative analysis of epileptic spikes.

Clinical Neurophysiology