Study of functional connectivity during anesthesia based on sparse partial least squares.
10.7507/1001-5515.201904052
- Author:
Fan WU
1
,
2
;
Zhongyi JIANG
1
,
2
;
Hui BI
1
,
2
;
Jun ZHANG
3
;
Shitong LI
4
;
Ling ZOU
1
,
2
Author Information
1. School of Information Science and Engineering, Changzhou University, Changzhou, Jiangsu 213164, P.R.China
2. Changzhou Key Laboratory of Biomedical Information Technology, Changzhou, Jiangsu 213164, P.R.China.
3. Department of Anesthesiology, Cancer Hospital of Fudan University, Shanghai 200032, P.R.China.
4. Department of Anesthesiology, Huashan Hospital of Fudan University, Shanghai 200040, P.R.China.
- Publication Type:Journal Article
- Keywords:
functional connectivity;
network analysis;
sparse partial least squares;
state of consciousness during anesthesia;
synchronization likelihood
- From:
Journal of Biomedical Engineering
2020;37(3):419-426
- CountryChina
- Language:Chinese
-
Abstract:
Anesthesia consciousness monitoring is an important issue in basic neuroscience and clinical applications, which has received extensive attention. In this study, in order to find the indicators for monitoring the state of clinical anesthesia, a total of 14 patients undergoing general anesthesia were collected for 5 minutes resting electroencephalogram data under three states of consciousness (awake, moderate and deep anesthesia). Sparse partial least squares (SPLS) and traditional synchronized likelihood (SL) are used to calculate brain functional connectivity, and the three conscious states before and after anesthesia were distinguished by the connection features. The results show that through the whole brain network analysis, SPLS and traditional SL method have the same trend of network parameters in different states of consciousness, and the results obtained by SPLS method are statistically significant ( <0.05). The connection features obtained by the SPLS method are classified by the support vector machine, and the classification accuracy is 87.93%, which is 7.69% higher than that of the connection feature classification obtained by SL method. The results of this study show that the functional connectivity based on the SPLS method has better performance in distinguishing three kinds of consciousness states, and may provides a new idea for clinical anesthesia monitoring.
