Department of Computer Engineering, Gachon University, Sungnam-si 13306, Korea.
Department of IT Convergence Engineering, Gachon University, Sungnam-si 13306, Korea.
Sensors (Basel). 2020 Nov 15;20(22):6526. doi: 10.3390/s20226526.
To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG's asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG's asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients.
为了进行客观的抑郁诊断，已经进行了许多基于机器学习和深度学习并使用脑电图（EEG）的研究。大多数研究都依赖于一维原始数据，并需要精细的特征提取。为了解决这个问题，在脑电图可视化研究领域，短时傅里叶变换（STFT）、小波和相干性通常被用作将脑电图数据转换为 2D 图像的方法。然而，我们从脑电图的不对称性被认为是抑郁症的主要生物标志物之一的概念出发，设计了一种新方法。本研究提出了一种深度不对称方法，将 EEG 的不对称特征转换为矩阵图像，并将其用作卷积神经网络的输入。在 alpha 波段的不对称矩阵图像达到了 98.85%的准确率，优于之前研究中提出的大多数方法。本研究表明，所提出的方法可以成为筛选重度抑郁症患者的有效工具。
