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

Abstract Concussion 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 dis...

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Autores principales:	Karun Thanjavur, Arif Babul, Brandon Foran, Maya Bielecki, Adam Gilchrist, Dionissios T. Hristopulos, Leyla R. Brucar, Naznin Virji-Babul
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/32cda9dbc07a4877ba9647ec30973506
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spelling	oai:doaj.org-article:32cda9dbc07a4877ba9647ec309735062021-12-02T17:52:32ZRecurrent neural network-based acute concussion classifier using raw resting state EEG data10.1038/s41598-021-91614-42045-2322https://doaj.org/article/32cda9dbc07a4877ba9647ec309735062021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91614-4https://doaj.org/toc/2045-2322Abstract Concussion 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.Karun ThanjavurArif BabulBrandon ForanMaya BieleckiAdam GilchristDionissios T. HristopulosLeyla R. BrucarNaznin Virji-BabulNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Karun Thanjavur Arif Babul Brandon Foran Maya Bielecki Adam Gilchrist Dionissios T. Hristopulos Leyla R. Brucar Naznin Virji-Babul Recurrent neural network-based acute concussion classifier using raw resting state EEG data
description	Abstract Concussion 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.
format	article
author	Karun Thanjavur Arif Babul Brandon Foran Maya Bielecki Adam Gilchrist Dionissios T. Hristopulos Leyla R. Brucar Naznin Virji-Babul
author_facet	Karun Thanjavur Arif Babul Brandon Foran Maya Bielecki Adam Gilchrist Dionissios T. Hristopulos Leyla R. Brucar Naznin Virji-Babul
author_sort	Karun Thanjavur
title	Recurrent neural network-based acute concussion classifier using raw resting state EEG data
title_short	Recurrent neural network-based acute concussion classifier using raw resting state EEG data
title_full	Recurrent neural network-based acute concussion classifier using raw resting state EEG data
title_fullStr	Recurrent neural network-based acute concussion classifier using raw resting state EEG data
title_full_unstemmed	Recurrent neural network-based acute concussion classifier using raw resting state EEG data
title_sort	recurrent neural network-based acute concussion classifier using raw resting state eeg data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/32cda9dbc07a4877ba9647ec30973506
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Recurrent neural network-based acute concussion classifier using raw resting state EEG data

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