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

TY - JOUR

T1 - A novel scheme for the validation of an automated classification method for epileptic spikes by comparison with multiple observers

AU - Sharma, Niraj K.

AU - Pedreira, Carlos

AU - Centeno, Maria

AU - Chaudhary, Umair J.

AU - Wehner, Tim

AU - França, Lucas G.S.

AU - Yadee, Tinonkorn

AU - Murta, Teresa

AU - Leite, Marco

AU - Vos, Sjoerd B.

AU - Ourselin, Sebastien

AU - Diehl, Beate

AU - Lemieux, Louis

PY - 2017/7/1

Y1 - 2017/7/1

N2 - 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.

AB - 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.

KW - Automated spike classification

KW - Information theory

KW - Interictal spike classification

KW - Intracranial EEG

UR - http://www.scopus.com/inward/record.url?scp=85019634498&partnerID=8YFLogxK

U2 - 10.1016/j.clinph.2017.04.016

DO - 10.1016/j.clinph.2017.04.016

M3 - Article

C2 - 28531810

AN - SCOPUS:85019634498

SN - 1388-2457

VL - 128

SP - 1246

EP - 1254

JO - Clinical Neurophysiology

JF - Clinical Neurophysiology

IS - 7

ER -