Tissue conductivity estimation in two-dimension head model based on support vector machine.
Estimating head tissue conductivity for each layer is a high dimensional, non-linear and ill-posed problem which is part of Electrical Impedance Tomography (EIT) inverse problem. Traditional methods have many difficulties in resolving this problem. Support Vector Machine (SVM) based on Statistical Learning Theory (SLT) is a new kind of learning method including Support Vector Classification (SVC) and Support Vector Regression (SVR). A new method using SVR is proposed to solve the problem in multi-input and multi-output system named Multi-SVM (MSVM). Tissue conductivity for each layer in 2-D head model is estimated effectively by MSVM. Compared with wavelet neural network method, MSVM not only obtains higher accuracy of learning, it also has greater generalization ability and faster computing speed as our experiment demonstrates.
Wu
Youxi
Y
School of Computer Science & Software, Hebei University of Technology, Tianjin, China. wuc567@163.com
Guo
Lei
L
Dong
Guoya
G
Wu
Qing
Q
Shen
Xueqin
X
Xu
Guizhi
G
Yan
Weili
W
D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D017097:Electric Impedance; D006257:Head / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014054:Tomography / Q000379:methods*