A Method of Depression Recognition Based on Visual Information

作者：Li, Mi; Liu, Xingwang; Lu, Shengfu^*; Wang, Xiaodong; Zhong, Ning^*

来源：Journal of Medical Imaging and Health Informatics, 2017, 7(7): 1572-1579.

DOI：10.1166/jmihi.2017.2168

• Depression Recognition
• Particle Swarm Optimization (PSO)
• Support Vector Machine (SVM)
• Adaptive Mutation

This study proposed a method to solve the problems existing in depression recognition, which is based on visual information, improved particle swarm optimization algorithm (PSO) and support vector machine (SVM). The PSO algorithm easily falls into local optimums; therefore, to solve the problem, we proposed an adaptive mutation PSO algorithm (AMPSO) to balance the capability of local exploitation and global exploration, thus creating a classification model with optimal parameters. First, we used no-iterative algorithms the kernel ridge regression and random forest to classify the depression and normal. Then, we compared the recognition accuracy using different PSO algorithms and found the visual information accuracy of the AMPSO algorithm for the SVM classifier to be the highest. Our research is of an important reference value for the establishment of methods for depression recognition with clinical applications.

• 出版日期2017-11
• 单位北京工业大学