IEEE J Biomed Health Inform. 2021 Jul;25(7):2545-2556. doi: 10.1109/JBHI.2020.3045718. Epub 2021 Jul 27.
Depression is a mental disorder with emotional and cognitive dysfunction. The main clinical characteristic of depression is significant and persistent low mood. As reported, depression is a leading cause of disability worldwide. Moreover, the rate of recognition and treatment for depression is low. Therefore, the detection and treatment of depression are urgent. Multichannel electroencephalogram (EEG) signals, which reflect the working status of the human brain, can be used to develop an objective and promising tool for augmenting the clinical effects in the diagnosis and detection of depression. However, when a large number of EEG channels are acquired, the information redundancy and computational complexity of the EEG signals increase; thus, effective channel selection algorithms are required not only for machine learning feasibility, but also for practicality in clinical depression detection. Consequently, we propose an optimal channel selection method for EEG-based depression detection via kernel-target alignment (KTA) to effectively resolve the abovementioned issues. In this method, we consider a modified version KTA that can measure the similarity between the kernel matrix for channel selection and the target matrix as an objective function and optimize the objective function by a proposed optimal channel selection strategy. Experimental results on two EEG datasets show that channel selection can effectively increase the classification performance and that even if we rely only on a small subset of channels, the results are still acceptable. The selected channels are in line with the expected latent cortical activity patterns in depression detection. Moreover, the experimental results demonstrate that our method outperforms the state-of-the-art channel selection approaches.
抑郁症是一种伴有情绪和认知功能障碍的精神障碍。抑郁症的主要临床特征是显著而持续的情绪低落。据报道，抑郁症是全球导致残疾的主要原因之一。此外，抑郁症的识别和治疗率较低。因此，对抑郁症的检测和治疗迫在眉睫。多通道脑电图（EEG）信号反映了人脑的工作状态，可用于开发一种客观且有前途的工具，以增强抑郁症诊断和检测的临床效果。然而，当获取大量 EEG 通道时，EEG 信号的信息冗余和计算复杂度会增加；因此，需要有效的通道选择算法，不仅要考虑机器学习的可行性，还要考虑在临床抑郁症检测中的实用性。因此，我们提出了一种基于核目标对准（KTA）的 EEG 抑郁症检测的最优通道选择方法，以有效解决上述问题。在该方法中，我们考虑了一种改进的 KTA，它可以将通道选择的核矩阵与目标矩阵之间的相似性作为目标函数进行度量，并通过提出的最优通道选择策略对目标函数进行优化。在两个 EEG 数据集上的实验结果表明，通道选择可以有效地提高分类性能，即使我们只依赖于一小部分通道，结果仍然可以接受。选择的通道符合抑郁症检测中预期的潜在皮质活动模式。此外，实验结果表明，我们的方法优于最先进的通道选择方法。
