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

Research output: Contribution to journalArticlepeer-review

Niraj K. 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

Original languageEnglish
Pages (from-to)1246-1254
Number of pages9
JournalClinical Neurophysiology
Issue number7
Early online date4 May 2017
Accepted/In press19 Apr 2017
E-pub ahead of print4 May 2017
Published1 Jul 2017

King's Authors


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 >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. 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.

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