TY - JOUR
T1 - Machine learning for detection of interictal epileptiform discharges
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:
© 2021 International Federation of Clinical Neurophysiology
PY - 2021/7
Y1 - 2021/7
N2 - The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis of the EEG. Visual assessment is, however, time consuming and prone to subjectivity, leading to a high misdiagnosis rate and motivating the development of automated approaches. Research towards automating IED detection started 45 years ago. Approaches range from mimetic methods to deep learning techniques. We review different approaches to IED detection, discussing their performance and limitations. Traditional machine learning and deep learning methods have yielded the best results so far and their application in the field is still growing. Standardization of datasets and outcome measures is necessary to compare models more objectively and decide which should be implemented in a clinical setting.
AB - The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis of the EEG. Visual assessment is, however, time consuming and prone to subjectivity, leading to a high misdiagnosis rate and motivating the development of automated approaches. Research towards automating IED detection started 45 years ago. Approaches range from mimetic methods to deep learning techniques. We review different approaches to IED detection, discussing their performance and limitations. Traditional machine learning and deep learning methods have yielded the best results so far and their application in the field is still growing. Standardization of datasets and outcome measures is necessary to compare models more objectively and decide which should be implemented in a clinical setting.
KW - Automated detection
KW - Convolutional neural networks
KW - Deep learning
KW - Electroencephalogram
KW - Interictal epileptiform discharges
KW - Machine learning
KW - UT-Hybrid-D
UR - http://www.scopus.com/inward/record.url?scp=85107625614&partnerID=8YFLogxK
U2 - 10.1016/j.clinph.2021.02.403
DO - 10.1016/j.clinph.2021.02.403
M3 - Review article
C2 - 34023625
AN - SCOPUS:85107625614
SN - 1388-2457
VL - 132
SP - 1433
EP - 1443
JO - Clinical neurophysiology
JF - Clinical neurophysiology
IS - 7
ER -