Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors

Artificial neural networks (ANNs) are showing increasing promise as decision support tools in medicine and particularly in neuroscience and neuroimaging. Recently, there has been increasing work on using neural networks to classify individuals with concussion using electroencephalography (EEG) data....

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Karun Thanjavur, Dionissios T. Hristopulos, Arif Babul, Kwang Moo Yi, Naznin Virji-Babul
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	concussion mild traumatic brain injury resting state EEG adolescents machine learning deep learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/cebee3837ad3469795943d13bfc65f98
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:cebee3837ad3469795943d13bfc65f98
record_format	dspace
spelling	oai:doaj.org-article:cebee3837ad3469795943d13bfc65f982021-11-30T18:11:46ZDeep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors1662-516110.3389/fnhum.2021.734501https://doaj.org/article/cebee3837ad3469795943d13bfc65f982021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnhum.2021.734501/fullhttps://doaj.org/toc/1662-5161Artificial neural networks (ANNs) are showing increasing promise as decision support tools in medicine and particularly in neuroscience and neuroimaging. Recently, there has been increasing work on using neural networks to classify individuals with concussion using electroencephalography (EEG) data. However, to date the need for research grade equipment has limited the applications to clinical environments. We recently developed a deep learning long short-term memory (LSTM) based recurrent neural network to classify concussion using raw, resting state data using 64 EEG channels and achieved high accuracy in classifying concussion. Here, we report on our efforts to develop a clinically practical system using a minimal subset of EEG sensors. EEG data from 23 athletes who had suffered a sport-related concussion and 35 non-concussed, control athletes were used for this study. We tested and ranked each of the original 64 channels based on its contribution toward the concussion classification performed by the original LSTM network. The top scoring channels were used to train and test a network with the same architecture as the previously trained network. We found that with only six of the top scoring channels the classifier identified concussions with an accuracy of 94%. These results show that it is possible to classify concussion using raw, resting state data from a small number of EEG sensors, constituting a first step toward developing portable, easy to use EEG systems that can be used in a clinical setting.Karun ThanjavurDionissios T. HristopulosArif BabulKwang Moo YiNaznin Virji-BabulNaznin Virji-BabulFrontiers Media S.A.articleconcussionmild traumatic brain injuryresting state EEGadolescentsmachine learningdeep learningNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Human Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	concussion mild traumatic brain injury resting state EEG adolescents machine learning deep learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	concussion mild traumatic brain injury resting state EEG adolescents machine learning deep learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Karun Thanjavur Dionissios T. Hristopulos Arif Babul Kwang Moo Yi Naznin Virji-Babul Naznin Virji-Babul Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors
description	Artificial neural networks (ANNs) are showing increasing promise as decision support tools in medicine and particularly in neuroscience and neuroimaging. Recently, there has been increasing work on using neural networks to classify individuals with concussion using electroencephalography (EEG) data. However, to date the need for research grade equipment has limited the applications to clinical environments. We recently developed a deep learning long short-term memory (LSTM) based recurrent neural network to classify concussion using raw, resting state data using 64 EEG channels and achieved high accuracy in classifying concussion. Here, we report on our efforts to develop a clinically practical system using a minimal subset of EEG sensors. EEG data from 23 athletes who had suffered a sport-related concussion and 35 non-concussed, control athletes were used for this study. We tested and ranked each of the original 64 channels based on its contribution toward the concussion classification performed by the original LSTM network. The top scoring channels were used to train and test a network with the same architecture as the previously trained network. We found that with only six of the top scoring channels the classifier identified concussions with an accuracy of 94%. These results show that it is possible to classify concussion using raw, resting state data from a small number of EEG sensors, constituting a first step toward developing portable, easy to use EEG systems that can be used in a clinical setting.
format	article
author	Karun Thanjavur Dionissios T. Hristopulos Arif Babul Kwang Moo Yi Naznin Virji-Babul Naznin Virji-Babul
author_facet	Karun Thanjavur Dionissios T. Hristopulos Arif Babul Kwang Moo Yi Naznin Virji-Babul Naznin Virji-Babul
author_sort	Karun Thanjavur
title	Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors
title_short	Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors
title_full	Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors
title_fullStr	Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors
title_full_unstemmed	Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors
title_sort	deep learning recurrent neural network for concussion classification in adolescents using raw electroencephalography signals: toward a minimal number of sensors
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/cebee3837ad3469795943d13bfc65f98
work_keys_str_mv	AT karunthanjavur deeplearningrecurrentneuralnetworkforconcussionclassificationinadolescentsusingrawelectroencephalographysignalstowardaminimalnumberofsensors AT dionissiosthristopulos deeplearningrecurrentneuralnetworkforconcussionclassificationinadolescentsusingrawelectroencephalographysignalstowardaminimalnumberofsensors AT arifbabul deeplearningrecurrentneuralnetworkforconcussionclassificationinadolescentsusingrawelectroencephalographysignalstowardaminimalnumberofsensors AT kwangmooyi deeplearningrecurrentneuralnetworkforconcussionclassificationinadolescentsusingrawelectroencephalographysignalstowardaminimalnumberofsensors AT nazninvirjibabul deeplearningrecurrentneuralnetworkforconcussionclassificationinadolescentsusingrawelectroencephalographysignalstowardaminimalnumberofsensors AT nazninvirjibabul deeplearningrecurrentneuralnetworkforconcussionclassificationinadolescentsusingrawelectroencephalographysignalstowardaminimalnumberofsensors
_version_	1718406409900523520

Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors

Ejemplares similares