Analysis of Feature Fusion Strategies of Resting-State Brain Functional Connectivity Network in Patients with Negative Temporal Lobe Epilepsy
Resting-state fMRI(rfMRI)can provide abnormal functional indicators by the functional connectivity(FC)analysis,however,the features redundancy would affect the classification precise.To address this issue,a feature fusion strategy combining specificity index model with discriminant correlation analysis(DCA)was proposed in this study to improve the identifying accuracy for patients with MRI-negative temporal lobe epilepsy.Firstly,the rfMRI data of 20 patients and 20 healthy people were preprocessed.Taking the healthy group as a control,two specificity index models were constructed by the conventional FC of pearson correlation and the network FC of graph theory.Secondly,both minimum redundancy maximum relevance(mRMR)and independent sample t test were used to eliminate redundant features,and DCA method was used to fuse feature.Finally,three machine-learning classifiers such as k-nearest neighbor(KNN),support vector machine(SVM)and logistic regression(LR)were used to validate our feature fusion method,and the nested stratification cross validations,such as 10 times 10 fold and 10 times 5 fold were used to evaluate the performance of three classifiers.The fusion feature of DC A could achieve the recognition rate of 91.25％～92.5％,higher than non-fusion strategies.In conclusion,the feature fusion strategy proposed in this paper could effectively deal with the redundant information and enhance feature discrimination.This work may provide new thoughts for the identification for MRI-negative temporal lobe epilepsy.
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