Abstract
The diagnosis of depression is a critical topic in the medical field. For years, the electroencephalogram (EEG) has been considered an objective and cost-effective detection tool. However, most studies on depression recognition models tend to extract information solely from the original temporal scale of EEG signals, ignoring the usage of coarse scales. This study aims to explore the feasibility of multiscale analysis for a depression recognition model and to research its temporal characteristics. Based on two types of multiscale entropy, this paper constructs a machine learning model using classifiers including LDA, LR, RBF-SVM, and KNN. The relation between the temporal scale and model performance was examined through mathematical analysis. The experimental results showed that the highest classification accuracy achieved was 96.42% with KNN at scale 3. Among various classifiers, scales 3 and 9 outperformed other scales. The model performance is correlated with the scale variation. Within a finite range, an optimal scale likely exists. The algorithm complexity is linearly related to the temporal scale. By accepting predictable computational costs, a stable improvement in model performance can be achieved. This multiscale analysis is practical in building and optimizing a depression recognition model. Further investigation of the relation between the temporal scale and model capabilities could advance the application of computer-assisted diagnosis.