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

Frontiers in Human Neuroscience

Kwang Moo Yi¹  Naznin Virji-Babul²  Arif Babul³  Karun Thanjavur³  Dionissios T. Hristopulos⁵ 

[1] Department of Computer Science, University of British Columbia, Vancouver, BC, Canada;Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada;Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada;Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada;School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece;

关键词: concussion;    mild traumatic brain injury;    resting state EEG;    adolescents;    machine learning;    deep learning;   

DOI  :  10.3389/fnhum.2021.734501

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown