TY - JOUR
T1 - Ultrafast review of ambulatory EEGs with deep learning
AU - da Silva Lourenço, Catarina
AU - Tjepkema-Cloostermans, Marleen C.
AU - van Putten, Michel J.A.M.
N1 - Funding Information:
This research was funded by the Epilepsiefonds Foundation , grant number WAR16-08 .
Publisher Copyright:
© 2023 International Federation of Clinical Neurophysiology
PY - 2023/10
Y1 - 2023/10
N2 - Objective: Interictal epileptiform discharges (IED) are hallmark biomarkers of epilepsy which are typically detected through visual analysis. Deep learning has shown potential in automating IED detection, which could reduce the burden of visual analysis in clinical practice. This is particularly relevant for ambulatory electroencephalograms (EEGs), as these entail longer review times. Methods: We applied a previously trained neural network to an independent dataset of 100 ambulatory EEGs (average duration 20.6 h). From these, 42 EEGs contained IEDs, 25 were abnormal without IEDs and 33 were normal. The algorithm flagged 2 second epochs that it considered IEDs. The EEGs were provided to an expert, who used NeuroCenter EEG to review the recordings. The expert concluded if each recording contained IEDs, and was timed during the process. Results: The conclusion of the reviewer was the same as the EEG report in 97% of the recordings. Three EEGs contained IEDs that were not detected based on the flagged epochs. Review time for the 100 EEGs was approximately 4 h, with half of the recordings taking <2 minutes to review. Conclusions: Our network can be used to reduce time spent on visual analysis in the clinic by 50–75 times with high reliability. Significance: Given the large time reduction potential and high success rate, this algorithm can be used in the clinic to aid in visual analysis.
AB - Objective: Interictal epileptiform discharges (IED) are hallmark biomarkers of epilepsy which are typically detected through visual analysis. Deep learning has shown potential in automating IED detection, which could reduce the burden of visual analysis in clinical practice. This is particularly relevant for ambulatory electroencephalograms (EEGs), as these entail longer review times. Methods: We applied a previously trained neural network to an independent dataset of 100 ambulatory EEGs (average duration 20.6 h). From these, 42 EEGs contained IEDs, 25 were abnormal without IEDs and 33 were normal. The algorithm flagged 2 second epochs that it considered IEDs. The EEGs were provided to an expert, who used NeuroCenter EEG to review the recordings. The expert concluded if each recording contained IEDs, and was timed during the process. Results: The conclusion of the reviewer was the same as the EEG report in 97% of the recordings. Three EEGs contained IEDs that were not detected based on the flagged epochs. Review time for the 100 EEGs was approximately 4 h, with half of the recordings taking <2 minutes to review. Conclusions: Our network can be used to reduce time spent on visual analysis in the clinic by 50–75 times with high reliability. Significance: Given the large time reduction potential and high success rate, this algorithm can be used in the clinic to aid in visual analysis.
KW - Ambulatory EEG
KW - Deep Learning
KW - EEG
KW - Epilepsy
KW - Interictal Epileptiform Discharges
KW - Time Reduction
KW - UT-Hybrid-D
UR - http://www.scopus.com/inward/record.url?scp=85166569317&partnerID=8YFLogxK
U2 - 10.1016/j.clinph.2023.07.005
DO - 10.1016/j.clinph.2023.07.005
M3 - Article
C2 - 37541076
AN - SCOPUS:85166569317
SN - 1388-2457
VL - 154
SP - 43
EP - 48
JO - Clinical neurophysiology
JF - Clinical neurophysiology
ER -