TY - GEN
T1 - Classification of Patients with Disorder of Consciousness Based on DTI Sequence Analysis
AU - Song, Hong
AU - Li, Qiang
AU - Li, Shixiong
AU - Kang, Wei
AU - Yang, Jian
AU - Yang, Yi
AU - He, Jianghong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/22
Y1 - 2017/11/22
N2 - In this paper, a method is proposed for classification of patients with disorder of consciousness (DOC) based on the diffusion tensor imaging (DTI) sequences analysis. The patients are divided into vegetative state (VS) and minimally consciousness state (MCS). Firstly, tract-based spatial statistics (TBSS) was applied to find the regions of interest (ROIs), and the values of fractional anisotropy (FA), mean diffusivity (MD) of ROIs were calculated subsequently. Secondly, statistical analysis, including t-test and Spearman correlation analysis were used to obtain the parameters with significant difference between VS and MCS and to extract the parameters significantly correlated to Coma Recovery Scale-Revised (CRS-R) scores. Finally, a classifier based on support vector machine (SVM) was trained with parameters of ROIs. Results show that a 92.31% accuracy was achieved with age and gender as extra classification features, and the confidence of classification result can be used to evaluate the level of consciousness of patients.
AB - In this paper, a method is proposed for classification of patients with disorder of consciousness (DOC) based on the diffusion tensor imaging (DTI) sequences analysis. The patients are divided into vegetative state (VS) and minimally consciousness state (MCS). Firstly, tract-based spatial statistics (TBSS) was applied to find the regions of interest (ROIs), and the values of fractional anisotropy (FA), mean diffusivity (MD) of ROIs were calculated subsequently. Secondly, statistical analysis, including t-test and Spearman correlation analysis were used to obtain the parameters with significant difference between VS and MCS and to extract the parameters significantly correlated to Coma Recovery Scale-Revised (CRS-R) scores. Finally, a classifier based on support vector machine (SVM) was trained with parameters of ROIs. Results show that a 92.31% accuracy was achieved with age and gender as extra classification features, and the confidence of classification result can be used to evaluate the level of consciousness of patients.
KW - diffusion tensor imaging
KW - minimally conscious state
KW - support vector machine
KW - tract-based spatial statistics
KW - vegetative state
UR - http://www.scopus.com/inward/record.url?scp=85041655174&partnerID=8YFLogxK
U2 - 10.1109/SmartCloud.2017.50
DO - 10.1109/SmartCloud.2017.50
M3 - Conference contribution
AN - SCOPUS:85041655174
T3 - Proceedings - 2nd IEEE International Conference on Smart Cloud, SmartCloud 2017
SP - 268
EP - 272
BT - Proceedings - 2nd IEEE International Conference on Smart Cloud, SmartCloud 2017
A2 - Qiu, Meikang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Smart Cloud, SmartCloud 2017
Y2 - 3 November 2017 through 5 November 2017
ER -