PMID- 32260320
OWN - NLM
STAT- MEDLINE
DCOM- 20200410
LR  - 20220110
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 20
IP  - 7
DP  - 2020 Apr 4
TI  - Investigation of Machine Learning Approaches for Traumatic Brain Injury 
      Classification via EEG Assessment in Mice.
LID - 10.3390/s20072027 [doi]
LID - 2027
AB  - Due to the difficulties and complications in the quantitative assessment of 
      traumatic brain injury (TBI) and its increasing relevance in today's world, 
      robust detection of TBI has become more significant than ever. In this work, we 
      investigate several machine learning approaches to assess their performance in 
      classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms 
      such as decision trees (DT), random forest (RF), neural network (NN), support 
      vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network 
      (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data 
      from those of the control group in wake stages for different epoch lengths. 
      Average power in different frequency sub-bands and alpha:theta power ratio in EEG 
      were used as input features for machine learning approaches. Results in this 
      mouse model were promising, suggesting similar approaches may be applicable to 
      detect TBI in humans in practical scenarios.
FAU - Vishwanath, Manoj
AU  - Vishwanath M
AD  - Department of Electrical Engineering and Computer Science, University of 
      California, Irvine, CA 92607, USA.
FAU - Jafarlou, Salar
AU  - Jafarlou S
AD  - Erik Jonsson School of Engineering and Computer Science, Dallas, TX 75201, USA.
FAU - Shin, Ikhwan
AU  - Shin I
AD  - Department of Electrical Engineering and Computer Science, University of 
      California, Irvine, CA 92607, USA.
FAU - Lim, Miranda M
AU  - Lim MM
AD  - VA Portland Health Care System, Portland, OR 97239, USA.
AD  - Departments of Neurology, Behavioral Neuroscience, Medicine, and Oregon Institute 
      of Occupational Health Sciences, Oregon Health & Science University, Portland, OR 
      97239, USA.
FAU - Dutt, Nikil
AU  - Dutt N
AD  - Department of Electrical Engineering and Computer Science, University of 
      California, Irvine, CA 92607, USA.
AD  - Department of Computer Science, University of California, Irvine, CA 92607, USA.
FAU - Rahmani, Amir M
AU  - Rahmani AM
AD  - Department of Computer Science, University of California, Irvine, CA 92607, USA.
AD  - School of Nursing, University of California, Irvine, CA 92607, USA.
FAU - Cao, Hung
AU  - Cao H
AUID- ORCID: 0000-0003-4197-7208
AD  - Department of Electrical Engineering and Computer Science, University of 
      California, Irvine, CA 92607, USA.
AD  - Department of Biomedical Engineering, University of California, Irvine, CA 92607, 
      USA.
LA  - eng
GR  - 1917105/National Science Foundation/
PT  - Journal Article
DEP - 20200404
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Animals
MH  - Brain Injuries, Traumatic/*physiopathology
MH  - *Electroencephalography
MH  - *Machine Learning
MH  - Male
MH  - Mice
MH  - Mice, Inbred C57BL
MH  - Technology Assessment, Biomedical
PMC - PMC7180997
OTO - NOTNLM
OT  - electroencephalogram (EEG)
OT  - machine learning (ML)
OT  - traumatic brain Injury (TBI)
COIS- The authors declare no conflict of interest.
EDAT- 2020/04/09 06:00
MHDA- 2020/04/11 06:00
PMCR- 2020/04/01
CRDT- 2020/04/09 06:00
PHST- 2020/01/10 00:00 [received]
PHST- 2020/03/26 00:00 [revised]
PHST- 2020/03/31 00:00 [accepted]
PHST- 2020/04/09 06:00 [entrez]
PHST- 2020/04/09 06:00 [pubmed]
PHST- 2020/04/11 06:00 [medline]
PHST- 2020/04/01 00:00 [pmc-release]
AID - s20072027 [pii]
AID - sensors-20-02027 [pii]
AID - 10.3390/s20072027 [doi]
PST - epublish
SO  - Sensors (Basel). 2020 Apr 4;20(7):2027. doi: 10.3390/s20072027.