A Novel Scheme for the Validation of an Automated Classification Method for Epileptic Spikes by Comparison with Multiple Observers

Publication date: Available online 4 May 2017
Source:Clinical Neurophysiology
Author(s): Niraj Sharma, Carlos Pedreira, Maria Centeno, Umair J. Chaudhary, Tim Wehner, Lucas G.S. França, Tinonkorn Yadee, Teresa Murta, Marco Leite, Sjoerd B. Vos, Sebastien Ourselin, Beate Diehl, Louis Lemieux
ObjectiveTo 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.MethodFive 10-minute 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.ResultsThe overlap between all WC-human pairs and all human pairs was >80% for 3/5 patients and >58% for the other 2 patients demonstrating WC falling within inter-human variation. The average sensitivity of spike marking for WC was 91% and >87% for all three EEG reviewers. Finally, there was a strong visual and quantitative similarity between WC and EEG reviewers.ConclusionsWC performance is indistinguishable to that of EEG reviewers' suggesting it could be a valid clinical tool for the assessment of IEDs.SignificanceWC can be used to provide quantitative analysis of epileptic spikes.



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