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Title: Moving the field forward: detection of epileptiform abnormalities on scalp electroencephalography using deep learning-clinical application perspectives.
Austin Authors: Janmohamed, Mubeen;Nhu, Duong;Kuhlmann, Levin;Gilligan, Amanda K ;Tan, Chang Wei;Perucca, Piero ;O'Brien, Terence J;Kwan, Patrick
Affiliation: Comprehensive Epilepsy Program
Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC 3800, Australia
Neurosciences Clinical Institute, Epworth Healthcare Hospital, Melbourne, VIC 3121, Australia
Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
Department of Neurology, Alfred Health, Melbourne, VIC 3004, Australia
Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC 3050, Australia
Issue Date: 2022 2022
Publication information: Brain Communications 2022; 4(5): fcac218
Abstract: The application of deep learning approaches for the detection of interictal epileptiform discharges is a nascent field, with most studies published in the past 5 years. Although many recent models have been published demonstrating promising results, deficiencies in descriptions of data sets, unstandardized methods, variation in performance evaluation and lack of demonstrable generalizability have made it difficult for these algorithms to be compared and progress to clinical validity. A few recent publications have provided a detailed breakdown of data sets and relevant performance metrics to exemplify the potential of deep learning in epileptiform discharge detection. This review provides an overview of the field and equips computer and data scientists with a synopsis of EEG data sets, background and epileptiform variation, model evaluation parameters and an awareness of the performance metrics of high impact and interest to the trained clinical and neuroscientist EEG end user. The gold standard and inter-rater disagreements in defining epileptiform abnormalities remain a challenge in the field, and a hierarchical proposal for epileptiform discharge labelling options is recommended. Standardized descriptions of data sets and reporting metrics are a priority. Source code-sharing and accessibility to public EEG data sets will increase the rigour, quality and progress in the field and allow validation and real-world clinical translation.
DOI: 10.1093/braincomms/fcac218
Journal: Brain Communications
PubMed URL: 36092304
Type: Journal Article
Subjects: EEG
automated detection
deep learning
epileptiform abnormalities
Appears in Collections:Journal articles

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