Feature-level fusion based on spatial-temporal of pervasive EEG for depression recognition

Bingtao Zhang; Dan Wei; Guanghui Yan; Tao Lei; Haishu Cai; Zhifei Yang

doi:10.1016/j.cmpb.2022.107113

Feature-level fusion based on spatial-temporal of pervasive EEG for depression recognition

Comput Methods Programs Biomed. 2022 Nov:226:107113. doi: 10.1016/j.cmpb.2022.107113. Epub 2022 Sep 11.

Authors

Bingtao Zhang¹, Dan Wei², Guanghui Yan², Tao Lei³, Haishu Cai⁴, Zhifei Yang²

Affiliations

¹ School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China. Electronic address: zhangbingtao321@163.com.
² School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
³ School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, China.
⁴ School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.

PMID: 36103735
DOI: 10.1016/j.cmpb.2022.107113

Abstract

Background and objective: In view of the depression characteristics such as high prevalence, high disability rate, high fatality rate, and high recurrence rate, early identification and early intervention are the most effective methods to prevent irreversible damage of brain function over time. The traditional method of depression recognition based on questionnaires and interviews is time-consuming and labor-intensive, and heavily depends on the doctor's subjective experience. Therefore, accurate, convenient and effective recognition of depression has important social value and scientific significance.

Methods: This paper proposes a depression recognition framework based on feature-level fusion of spatial-temporal pervasive electroencephalography (EEG). Time series EEG data were collected by portable three-electrode EEG acquisition instrument, and mapped to a spatial complex network called visibility graph (VG). Then temporal EEG features and spatial VG metric features were extracted and selected. Based on the correlation between features and categories, the differences in contribution of individual feature are explored, and different contribution coefficients are assigned to different features as the data basis of feature-level fusion to ensure the diversity of data. A cascade forest model based on three different decision forests is designed to realize the efficient depression recognition using spatial-temporal feature-level fusion data.

Results: Experimental data were obtained from 26 depressed patients and 29 healthy controls (HC). The results of multiple control experiments show that compared with single type feature, feature-level fusion without contribution coefficient, and independent classifiers, the feature-level method with contribution coefficient of spatial-temporal has a stronger recognition ability of depression, and the highest accuracy is 92.48%.

Conclusion: Feature-level fusion method provides an effective computer-aided tool for rapid clinical diagnosis of depression.

Keywords: Depression recognition; Electroencephalography; Feature-level fusion; Visibility graph.

MeSH terms

Algorithms*
Depression* / diagnosis
Electrodes
Electroencephalography / methods
Humans
Time Factors