3DMKDR: 3D Multiscale Kernels CNN Model for Depression Recognition Based on EEG

Abstract

Depression has become a major health threat around the world, especially for older people, so the effective detection method for depression is a great public health challenge. Electroencephalogram (EEG) can be used as a biomarker to effectively explore depression recognition. Motivated by the studies that multiple smaller scale kernels could increase nonlinear expression compared to a larger kernel, this article proposes a model named the three-dimensional multiscale kernels convolutional neural network model for the depression disorder recognition (3DMKDR), which is a three-dimensional convolutional neural network model with multiscale convolutional kernels for depression recognition based on EEG signals. A three-dimensional structure of the EEG is built by extending one-dimensional feature sequences into a two-dimensional electrode matrix to excavate the related spatiotemporal information among electrodes and the collected electrode matrix. By the major depressive disorder (MDD) and the multi-modal open dataset for mental-disorder analysis (MODMA) datasets, the experiment shows that the accuracies of depression recognition are up to 99.86% and 98.01% in the subject-dependent experiment, and 95.80% and 82.27% in the subject-independent experiment, which are higher than alternative competitive methods. The experimental results demonstrate that the proposed 3DMKDR is potentially useful for depression recognition in older persons in the future.

Keywords

major depression disorder (MDD) / electroencephalogram (EEG) / three-dimensional convolutional neural network (3D-CNN) / spatiotemporal features