A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal

Abstract

Depression has gradually become the most common mental disorder in the world. The accuracy of its diagnosis may be affected by many factors, while the primary diagnosis seems to be difficult to define. Finding a way to identify depression by satisfying both objective and effective conditions is an urgent issue. In this paper, a strategy for predicting depression based on spatiotemporal features is proposed, and is expected to be used in the auxiliary diagnosis of depression. Firstly, electroencephalogram (EEG) signals were denoised through the filter to obtain the power spectra of the three corresponding frequency ranges, Theta, Alpha and Beta. Using orthogonal projection, the spatial positions of the electrodes were mapped to the brainpower spectrum, thereby obtaining three brain maps with spatial information. Then, the three brain maps were superimposed on a new brain map with frequency domain and spatial characteristics. A Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) were applied to extract the sequential feature. The proposed strategy was validated with a public EEG dataset, achieving an accuracy of 89.63% and an accuracy of 88.56% with the private dataset. The network had less complexity with only six layers. The results show that our strategy is credible, less complex and useful in predicting depression using EEG signals.

Applying Deep Learning on a Few EEG Electrodes during Resting State Reveals Depressive States: A Data Driven Study

“…Having fixed the time-window span to 15 s, the brain area CACC with its associated electrodes (FC2, AFz, F2) and the nonlinear features (PerEnt, SampEnt, SVDEntr, DFA, SpectEnt, Higuchi), a 10-fold cross-validation test harness to ML and DL classifiers was applied: logistic regression (LR), support vector machine (SVM) with radial basis function (RBF) kernel, multilayer perceptron (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), and newest CNN + GRU (CNNGRU) proposed classifier by Liu W. [ 11 ]. The accuracy was selected as a metric score.…”

“…Recent studies overcome some of the proposed pitfalls. For instance, a large study [ 11 ] adopting a DL approach was validated with both a private database and an external database, using 16 channels. The model extracted spatiotemporal features through a convolutional neural network (CNN), combined with a gated recurrent unit (GRU), the former extracting spatial and frequency features and the latter capturing time sequence aspects of the extracted features.…”

“…The current study aims to improve the classification of depression with nonlinear features, few electrodes, and standardized classifiers, as well as the model recently suggested by Liu et al [ 11 ]. In this study, EEG resting state data were collected from a group of participants with depressive state and a group of matched controls, aiming to choose the best classification algorithm with a reduced number of electrodes and limited time-window, and their generalization was tested for an external sample.…”

“… Long short-term memory (LSTM): a DL algorithm mainly used in sequential data analysis like natural language processing or time-series analysis. CNN + GRU (CNNGRU): a DL algorithm suggested by Liu et al [ 11 ] that combines a CNN as a feature extractor plus a gated recurrent unit (GRU), an improvement of LSTM. …”

“…CNN + GRU (CNNGRU): a DL algorithm suggested by Liu et al [ 11 ] that combines a CNN as a feature extractor plus a gated recurrent unit (GRU), an improvement of LSTM.…”