Interpretation of Frequency Channel-Based CNN on Depression Identification

Hengjin Ke; Cang Cai; Fengqin Wang; Fang Hu; Jiawei Tang; Yuxin Shi

doi:10.3389/fncom.2021.773147

Interpretation of Frequency Channel-Based CNN on Depression Identification

Front Comput Neurosci. 2021 Dec 27:15:773147. doi: 10.3389/fncom.2021.773147. eCollection 2021.

Authors

Hengjin Ke¹, Cang Cai², Fengqin Wang³, Fang Hu⁴, Jiawei Tang¹, Yuxin Shi¹

Affiliations

¹ Computer School, Hubei Polytechnic University, Huangshi, China.
² Faculty of Artificial Intelligence Education, Central China Normal University, Wuhan, China.
³ College of Physics and Electronics Science, Hubei Normal University, Huangshi, China.
⁴ Department of Clinical Laboratory, Huangshi Central Hospital, Edong Healthcare Group (Affiliated Hospital of Hubei Polytechnic University), Huangshi, China.

Abstract

Online end-to-end electroencephalogram (EEG) classification with high performance can assess the brain status of patients with Major Depression Disabled (MDD) and track their development status in time with minimizing the risk of falling into danger and suicide. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic non-stationarity determined by the evolution of brain states, (2) the lack of effective decoupling of the complex relationship between neural network and brain state during the attack of brain diseases. This study designs a Frequency Channel-based convolutional neural network (CNN), namely FCCNN, to accurately and quickly identify depression, which fuses the brain rhythm to the attention mechanism of the classifier with aiming at focusing the most important parts of data and improving the classification performance. Furthermore, to understand the complexity of the classifier, this study proposes a calculation method of information entropy based on the affinity propagation (AP) clustering partition to measure the complexity of the classifier acting on each channel or brain region. We perform experiments on depression evaluation to identify healthy and MDD. Results report that the proposed solution can identify MDD with an accuracy of 99±0.08%, the sensitivity of 99.07±0.05%, and specificity of 98.90±0.14%. Furthermore, the experiments on the quantitative interpretation of FCCNN illustrate significant differences between the frontal, left, and right temporal lobes of depression patients and the healthy control group.

Keywords: EEG classification; attention; convolutional neural network (CNN); depression; interpretation.