Depression is a common mental illness with widespread effects worldwide. At present, most relevant studies are performed by using free-viewexperiments to identify depression based on eye movements, which identify depression by analyzing the fixation patterns of subjects. However, technical and cost barriers make the rapid and objective detection of depression challenging. This study aimed to develop an objective and convenient method that utilizes eye tracking technology to detect depression. In this study, we designed an eye-tracking algorithm to localize pupil position and presented a variance entropy method to measure the fluctuating characteristics of eye movements. Video data were collected from 115 participants and 35 controls (mean age 24.99 years, standard deviation 6.14 years). The pupil position time series data were formed by the eye-tracking algorithm, and the variance entropy of the pupil position time series was calculated. Based on eye movement variance entropy, we used three machine learning models for classification detection: Logistic Regression, K-Nearest Neighbors, and Random Forest. The accuracy of machine learning in identifying whether a person had depression or not was 97.5%. The accuracy of the four class depression classification (severe, moderate, mild, and no depression) was 82.5%.The accuracy of identifying whether a person had anxiety or not was 95%. The accuracy of identifying whether a person had suicidal tendencies or not was 85%. The results showed that variance entropy based on the eye movement time series can be used as a valid bioindicator for detecting depression.

Depression is a prevalent and severe global mental disorder, yet its diagnosis and treatment encounter numerous challenges. This study introduces an efficient depression identification network, termed DNet. Our approach utilizes facial images and local facial images as crucial sources of data. Although people with depression may exhibit similar general facial expressions, subtle changes in specific facial areas (e.g., eyes, mouth) can be observed as the severity of depression increases. These changes are reflected in the models’ attention mechanisms, highlighting the differences between severity levels. To achieve higher recognition accuracy, a method is required to fuse advanced semantic features between local and global features. Therefore, we propose DNet, comprising two key components: the Feature Extraction Module (FEM) and the Vision Transformer (ViT) Block. Specifically, FEM introduces an attention mechanism that considers both channel and positional information of the feature Map. Two FEMs are employed to separately process facial and local facial images, extracting critical features to generate highly semantic information-rich feature Maps. Subsequently, the feature maps of both images are concatenated along the channel dimension, Using FPN feature fusion, and the ViT Block is utilized to comprehensively learn advanced semantic features of local and global information related to different facial expression regions. Finally, a 1× 1 convolution layer and a fully connected layer are applied to adjust feature channels, yielding more robust predictive results and ultimately outputting depression prediction scores.We experimentally validate the DNet network on the AVEC2014 dataset and our self-constructed CZ2023 dataset, obtaining results of MAE = 6.09, RMSE = 7.85, and MAE = 6.73, RMSE = 8.47, respectively. These results affirm the effectiveness of the proposed method.

The depth of anesthesia monitoring is helpful to guide administrations of general anesthetics during surgical procedures,however, the conventional 2-4 channels electroencephalogram (EEG) derived monitors have their limitations in monitoring conscious states due to low spatial resolution and suboptimal algorithm. In this study, 256-channel high-density EEG signals in 24 subjects receiving three types of general anesthetics (propofol, sevoflurane and ketamine) respectively were recorded both before and after anesthesia. The raw EEG signals were preprocessed by EEGLAB 14.0. Functional connectivity (FC) analysis by traditional coherence analysis (CA) method and a novel sparse representation (SR) method. And the network parameters, average clustering coefficient (ACC) and average shortest path length (ASPL) before and after anesthesia were calculated. The results show SR method find more significant FC differences in frontal and occipital cortices, and whole brain network (p0.05). Further, ASPL calculated by SR for whole brain connections in all of three anesthesia groups increased, which can be a unified EEG biomarker of general anesthetics-induced loss of consciousness (LOC). Therefore FC analysis based on SR analysis has better performance in distinguishing anesthetic-induced LOC from awake state.

An improved slime mold algorithm (IMSMA) is presented in this paper for a multiprocessor multitask fair scheduling problem, which aims to reduce the average processing time. An initial population strategy based on Bernoulli mapping reverse learning is proposed for the slime mold algorithm. A Cauchy mutation strategy is employed to escape local optima, and the boundary-check mechanism of the slime mold swarm is optimized. The boundary conditions of the slime mold population are transformed into nonlinear, dynamically changing boundaries. This adjustment strengthens the slime mold algorithm’s global search capabilities in early iterations and strengthens its local search capability in later iterations, which accelerates the algorithm’s convergence speed. Two unimodal and two multimodal test functions from the CEC2019 benchmark are chosen for comparative experiments. The experiment results show the algorithm’s robust convergence and its capacity to escape local optima. The improved slime mold algorithm is applied to the multiprocessor fair scheduling problem to reduce the average execution time on each processor. Numerical experiments showed that the IMSMA performs better than other algorithms in terms of precision and convergence effectiveness.