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Recurrent neural network-based acute concussion classifier using raw resting state EEG data

Thanjavur Karun, Babul, Arif, Foran Brandon, Bielecki Maya, Gilchrist Adam, Christopoulos Dionysios, Brucar Leyla R., Virji-Babul, Naznin, 1962-

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URIhttp://purl.tuc.gr/dl/dias/F6942C3C-98E2-4F93-AFF4-150C2A905E3E-
Identifierhttps://doi.org/10.1038/s41598-021-91614-4-
Identifierhttps://www.nature.com/articles/s41598-021-91614-4-
Languageen-
Extent19 pagesen
TitleRecurrent neural network-based acute concussion classifier using raw resting state EEG dataen
CreatorThanjavur Karunen
CreatorBabul, Arifen
CreatorForan Brandonen
CreatorBielecki Mayaen
CreatorGilchrist Adamen
CreatorChristopoulos Dionysiosen
CreatorΧριστοπουλος Διονυσιοςel
CreatorBrucar Leyla R.en
CreatorVirji-Babul, Naznin, 1962-en
PublisherSpringer Natureen
Content SummaryConcussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, the most significant challenge in concussion management is the lack of objective, clinically- accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Here, we report on our efforts to address this challenge. Specifically, we introduce a deep learning long short-term memory (LSTM)-based recurrent neural network that is able to distinguish between non-concussed and acute post-concussed adolescent athletes using only short (i.e. 90 s long) samples of resting state EEG data as input. The athletes were neither required to perform a specific task nor expected to respond to a stimulus during data collection. The acquired EEG data were neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and validated using data from 27 male, adolescent athletes with sports related concussion, benchmarked against 35 non-concussed adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of > 90% and achieved an ensemble median Area Under the Receiver Operating Characteristic Curve (ROC/AUC) equal to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state, raw EEG data. Our concussion classifier represents a promising first step towards the development of an easy-to-use, objective, brain-based, automatic classification of concussion at an individual level.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-01-11-
Date of Publication2021-
SubjectBrain injuriesen
SubjectMachine learningen
SubjectNeuroscienceen
Bibliographic CitationK. Thanjavur, A. Babul, B. Foran, M. Bielecki, A. Gilchrist, D. T. Hristopulos, L. R. Brucar, and N. Virji-Babul, “Recurrent neural network-based acute concussion classifier using raw resting state EEG data,” Sci. Rep., vol. 11, no. 1, June 2021, doi: 10.1038/s41598-021-91614-4.en

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