Content-based multiple evidence fusion on EEG and eye movements for mild depression recognition. (November 2022)

• Record Type: Journal Article
• Authors: Zhu, Jing; Wei, Shiqing; Xie, Xiannian; Yang, Changlin; Li, Yizhou; Li, Xiaowei; Hu, Bin
• Is Part Of: Computer methods and programs in biomedicine. Volume 226(2022)
• Publication Date: 2022-11
• Subjects: EEG -- Eye movement -- Depression recognition -- Multimodal fusion -- DS evidence theory; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107100
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract: Background and objective: Depression is a serious neurological disorder that has become a major health problem worldwide. The detection of mild depression is important for the diagnosis of depression in early stages. This research seeks to find a more accurate fusion model which can be used for mild depression detection using Electroencephalography and eye movement data. Methods: This study proposes a content-based multiple evidence fusion (CBMEF) method, which fuses EEG and eye movement data at decision level. The method mainly includes two modules, the classification performance matrix module and the dual-weight fusion module. The classification performance matrices of different modalities are estimated by Bayesian rule based on confusion matrix and Mahalanobis distance, and the matrices were used to correct the classification results. Then the relative conflict degree of each modality is calculated, and different weights are assigned to the above modalities at the decision fusion layer according to this conflict degree. Results: The experimental results show that the proposed method outperforms other fusion methods as well as the single modality results. The highest accuracies achieved 91.12%, and sensitivity, specificity and precision were 89.20%, 93.03%, 92.76%. Conclusions: The promising results showed the potential of the proposed approach for the detection of mild depression. The idea of introducing the classification performance matrix and the dual-weight model to multimodal biosignals fusion casts a new light on the researches of depression recognition.