TY - JOUR
T1 - A quantitative approach to evaluating interictal epileptiform discharges based on interpretable quantitative criteria
AU - Nascimento, Fábio A.
AU - Barfuss, Jaden D.
AU - Jaffe, Alex
AU - Brandon Westover, M.
AU - Jing, Jin
N1 - Publisher Copyright:
© 2022
PY - 2023/2
Y1 - 2023/2
N2 - Objective: To provide quantitative measures of the six International Federation of Clinical Neurophysiology (IFCN) criteria for interictal epileptiform discharge (IED) identification and estimate the likelihood of a candidate IED being epileptiform. Methods: We designed an algorithm to identify five fiducial landmarks (onset, peak, trough, slow-wave peak, offset) of a candidate IED, and from these to quantify the six IFCN features of IEDs. Another model was trained with these features to quantify the probability that the waveform is epileptiform and incorporated into a user-friendly interface. Results: The model's performance is excellent (area under the receiver operating characteristic curve (AUROC) = 0.88; calibration error 0.03) but lower than human experts (receiver operating characteristic (ROC) curve is below experts’ operating points) or a deep neural-network model (SpikeNet; AUCROC = 0.97; calibration error 0.04). The six features were all significant (p<0.001), but not equally important when determining potential epileptiform nature of candidate IEDs: waveform asymmetry was the most (coefficient 0.64) and duration the least discriminative (coefficient 0.09). Conclusions: Our approach quantifies the six IFCN criteria for IED identification and combines them in an easily interpretable, accessible fashion that accurately captures the likelihood that a candidate waveform is epileptiform. Significance: This model may assist clinical electroencephalographers decide whether candidate waveforms are epileptiform and may assist trainees learn to identify IEDs.
AB - Objective: To provide quantitative measures of the six International Federation of Clinical Neurophysiology (IFCN) criteria for interictal epileptiform discharge (IED) identification and estimate the likelihood of a candidate IED being epileptiform. Methods: We designed an algorithm to identify five fiducial landmarks (onset, peak, trough, slow-wave peak, offset) of a candidate IED, and from these to quantify the six IFCN features of IEDs. Another model was trained with these features to quantify the probability that the waveform is epileptiform and incorporated into a user-friendly interface. Results: The model's performance is excellent (area under the receiver operating characteristic curve (AUROC) = 0.88; calibration error 0.03) but lower than human experts (receiver operating characteristic (ROC) curve is below experts’ operating points) or a deep neural-network model (SpikeNet; AUCROC = 0.97; calibration error 0.04). The six features were all significant (p<0.001), but not equally important when determining potential epileptiform nature of candidate IEDs: waveform asymmetry was the most (coefficient 0.64) and duration the least discriminative (coefficient 0.09). Conclusions: Our approach quantifies the six IFCN criteria for IED identification and combines them in an easily interpretable, accessible fashion that accurately captures the likelihood that a candidate waveform is epileptiform. Significance: This model may assist clinical electroencephalographers decide whether candidate waveforms are epileptiform and may assist trainees learn to identify IEDs.
KW - EEG
KW - Education
KW - Epileptiform discharges
KW - Interictal epileptiform discharges
UR - http://www.scopus.com/inward/record.url?scp=85143537570&partnerID=8YFLogxK
U2 - 10.1016/j.clinph.2022.10.018
DO - 10.1016/j.clinph.2022.10.018
M3 - Article
C2 - 36473334
AN - SCOPUS:85143537570
SN - 1388-2457
VL - 146
SP - 10
EP - 17
JO - Clinical Neurophysiology
JF - Clinical Neurophysiology
ER -