A convolutional autoencoder for identification of mild traumatic brain injury

Fatemeh Koochaki, Laleh Najafizadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Mild traumatic brain injury (mTBI) is the most common form of traumatic brain injury (TBI), yet its timely diagnosis has remained challenging due to lack of established criteria and biomarkers. Widefield optical imaging of neuronal populations over the cerebral cortex in animals provides a unique opportunity to study injury-induced alterations of the brain function. Using a convolutional autoencoder (CAE), this paper aims to develop a framework for detecting mTBI from calcium imaging data to identify the most informative injury-related features. A support vector machine is then trained to classify healthy and injured subjects, and an average classification accuracy of 96.47% is obtained for the best case scenario. Our results suggest that the spatial features obtained through CAE can discriminate the injured and healthy brains better than naive convolutional neural network (CNN).

Original languageEnglish (US)
Title of host publication2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
PublisherIEEE Computer Society
Pages412-415
Number of pages4
ISBN (Electronic)9781728143378
DOIs
StatePublished - May 4 2021
Event10th International IEEE/EMBS Conference on Neural Engineering, NER 2021 - Virtual, Online, Italy
Duration: May 4 2021May 6 2021

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2021-May
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
Country/TerritoryItaly
CityVirtual, Online
Period5/4/215/6/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Mechanical Engineering

Fingerprint

Dive into the research topics of 'A convolutional autoencoder for identification of mild traumatic brain injury'. Together they form a unique fingerprint.

Cite this