Recognition Of Depression Research Articles

Baby blues, premenstrual syndrome and postpartum affective disorders: intersection of risk factors and reciprocal influences.

The aetiology and consequences of 'baby blues' (lower mood following childbirth) are yet to be sufficiently investigated with respect to an individual's clinical history. The primary aim of the study was to assess the symptoms of baby blues and the relevant risk factors, their associations with clinical history and premenstrual syndrome (PMS), and their possible contribution to the early recognition of postpartum depression (PPD). Beginning shortly after childbirth, 369 mothers were followed up for 12 weeks. Information related to their clinical history, PMS, depression, stress and mother-child attachment was collected. At 12 weeks, mothers were classified as non-depressed, or with either PPD or adjustment disorder. A correlation was found between the severity of baby blues and PMS (r = 0.397, P < 0.001), with both conditions increasing the possibility of adjustment disorder and PPD (baby blues: OR = 6.72, 95% CI 3.69-12.25; PMS: OR = 3.29, 95% CI 2.01-5.39). Baby blues and PMS independently predicted whether a mother would develop adjustment disorder or PPD after childbirth (χ2(64) = 198.16, P < 0.001). Among the non-depressed participants, baby blues were found to be associated with primiparity (P = 0.012), family psychiatric history (P = 0.001), PMS (P < 0.001) and childhood trauma (P = 0.017). Baby blues are linked to a number of risk factors and a history of PMS, with both conditions adding to the risk of PPD. The neuroendocrine effects on mood need be understood in the context of individual risk factors. The assessment of both baby blues and PMS symptoms within the first postpartum days may contribute to an early identification of PPD.

TSFFM: Depression detection based on latent association of facial and body expressions

Depression is a prevalent mental disorder worldwide. Early screening and treatment are crucial in preventing the progression of the illness. Existing emotion-based depression recognition methods primarily rely on facial expressions, while body expressions as a means of emotional expression have been overlooked. To aid in the identification of depression, we recruited 156 participants for an emotional stimulation experiment, gathering data on facial and body expressions. Our analysis revealed notable distinctions in facial and body expressions between the case group and the control group and a synergistic relationship between these variables. Hence, we propose a two-stream feature fusion model (TSFFM) that integrates facial and body features. The central component of TSFFM is the Fusion and Extraction (FE) module. In contrast to conventional methods such as feature concatenation and decision fusion, our approach, FE, places a greater emphasis on in-depth analysis during the feature extraction and fusion processes. Firstly, within FE, we carry out local enhancement of facial and body features, employing an embedded attention mechanism, eliminating the need for original image segmentation and the use of multiple feature extractors. Secondly, FE conducts the extraction of temporal features to better capture the dynamic aspects of expression patterns. Finally, we retain and fuse informative data from different temporal and spatial features to support the ultimate decision. TSFFM achieves an Accuracy and F1-score of 0.896 and 0.896 on the depression emotional stimulus dataset, respectively. On the AVEC2014 dataset, TSFFM achieves MAE and RMSE values of 5.749 and 7.909, respectively. Furthermore, TSFFM has undergone testing on additional public datasets to showcase the effectiveness of the FE module.

Clustering Based on Eye Tracking Data for Depression Recognition

Attention-based approach would be a good way of detecting depression, assisting medical diagnosis and treating the patients at risk earlier. In this paper, a new approach of recognizing depression is proposed, which avoids eye movement event identification and directly performs clustering based on eye tracking data to obtain Regions of Interesting (ROIs), and then conducts depression recognition modelling. Based on these, a novel spatio-temporal clustering algorithm was proposed, i.e. ROI Clustering with Deflection Elimination, which takes the noisy data into consideration to betterly describe attention patterns. On the dataset with 45 depression patients and 44 healthy controls, the proposed algorithm achieved the best classification accuracy of 76.25%, which has the potential to provide methodological reference on the assessment of mental disorders based on eye movements.

Maternal major depression during early pregnancy is associated with impaired child executive functioning at 4.5 years of age

BackgroundMaternal depression is a serious condition that effects up to 1 in 7 pregnancies. Despite evidence linking maternal depression to pregnancy complications and adverse fetal outcomes, there remain significant gaps in its identification and treatment. More work is needed to define the specific timing and severity of depression that most urgently requires intervention, where feasible, to protect maternal health and the developing fetus. ObjectiveTo examine whether the timing and severity of maternal depression and/or anxiety during pregnancy effects child executive functioning at age 4.5 years. Executive functioning in the pre-school years is a strong predictor of both school readiness and long term quality of life. Study DesignThis longitudinal observational pregnancy cohort study included a sample of 323 mother-child dyads taking part in the Ontario Birth Study, an open pregnancy cohort in Toronto, Ontario, Canada. Maternal symptoms of depression and anxiety were assessed at 12-16 and 28-32 weeks of pregnancy, and at the time of child testing at age 4.5 years, using the 4-item Patient Health Questionnaire. Child executive functioning was measured during a home visit using standardized computerized administration of the Flanker test (a measure of attention) and the Dimensional Change Card Sort (a measure of cognitive flexibility). Stepwise linear regressions, controlling for possible confounding variables, were used to assess the predictive value of continuous measures of maternal depression and/or anxiety symptoms at each assessment time on the Flanker and Dimensional Change Card Sort. Post-hoc general linear models assessed whether maternal depression severity categories (no symptoms, mild symptoms, probable major depressive disorder) were helpful in identifying children-at-risk. ResultsAcross all children, after controlling for potential confounds, greater maternal depressive symptoms at weeks 12-16 of pregnancy predicted worse performance on both the Flanker test (ΔR2=0.058, p < .001) and the Dimensional Change Card Sort (ΔR2 = 0.017, p = 0.018). Post-hoc general linear modelling further demonstrated that the children of mothers meeting screening criteria for major depression in early pregnancy scored 11.3 % lower on the Flanker test, and 9.8 % lower on the Dimensional Change Card Sort, than did children exposed to no maternal depressive symptoms in early pregnancy. Mild depressive symptoms had no significant effect on executive function scores. There was no significant effect of anxiety symptoms or maternal anti-depressant use in early pregnancy, nor of pandemic conditions or maternal symptoms in later pregnancy or at the time of child testing on either the Flanker or Dimensional Change Card Sort results. ConclusionThis is the first study to demonstrate that during weeks 12-16 of pregnancy, fetal exposure to maternal major depression, but not milder forms of depression, associates with impaired executive functioning in the pre-school years. Child executive functioning is critical for school readiness and predicts long-term quality of life. This emphasizes an urgent need to improve the recognition and treatment of maternal major depression, particularly in early pregnancy, to limit its negative effects on the patient and on child cognitive development.

Multi-feature deep supervised voiceprint adversarial network for depression recognition from speech

Depression can induce a range of physiological effects, leading to notable distinctions in the acoustic characteristics exhibited by individuals with depression as opposed to those without. Designing efficient algorithms to accurately identify depression through speech poses a formidable challenge. In this paper, we propose the Multi-Feature Deep Supervised Voiceprint Adversarial Network (MFDS-VAN) for audio-based depression recognition. The MFDS-VAN assimilates extracted acoustic features and the audio waveform, subsequently generating predictions regarding the depression score. In order to attain more robust and discriminative spatial–temporal features associated with depression, the Encoding Network module merges long-term and short-term acoustic features with the unprocessed audio waveform, while the Regression Network module enables the prediction of the depression score. The Deep Supervised Regression algorithm is designed by combining GE2E clustering and Huber regression for better network optimization. Furthermore, to enhance the representation of the MFDS-VAN while diminishing the influence of individual voiceprint information, we propose the Voiceprint Adversarial Network. Experimental results conducted on AVEC 2013, AVEC 2014, and AVEC 2017 datasets demonstrate that the MFDS-VAN significantly enhances robustness and performance in speech-based depression recognition. Our model achieves competitive results when compared to recent audio-based methodologies.

Construction of a resting EEG-based depression recognition model for college students and possible mechanisms of action of different types of exercise

ObjectivesTo investigate the method of resting EEG assessment of depressive symptoms in college students and to clarify the relationship between physical activity level and depressive symptoms in college students.MethodsUsing a cross-sectional study design, 140 current full-time college students were recruited to complete the Self-Rating Depression Scale and the International Physical Activity Questionnaire, and 10-min resting EEGs were obtained.Results1) The power values of δ and α2 in the central (C3, C4) and parietal (P3, P4) regions of depressed college students were significantly higher than those of normal college students. And the degree of lateralization of δ, θ, α1, and α2 in the prefrontal regions (F3, F4) of depressed college students was significantly higher than that of normal college students (all P < 0. 008). 2) The recall rate of the depression recognition model for college students based on resting EEG was 66.67%, the precision was 65.05%, and the AUCs of the training group and validation group were 0.791 and 0.786, respectively, with better detection effects. 3) The two indicators, δ (C3 + C4) and α1 (F4-F3), are significantly correlated with IPAQ scores, and among college students who engage in ball games most commonly, those with a higher level of physical activity have lower δ (C3 + C4) and higher α1 (F4-F3), while among those who engage in resistance training most commonly, higher levels of physical activity are associated with lower δ (C3 + C4).ConclusionThe resting EEG of depressed college students has a certain specificity that can objectively assess the risk of developing depressive symptoms in college students. Physical activity is associated with abnormal EEG signals of depressive symptoms. Different types of physical activity may modulate the relationship between physical activity levels and EEG indicators.

A novel study for depression detecting using audio signals based on graph neural network

Depression is a prevalent mental health disorder. The absence of specific biomarkers makes clinical diagnosis highly subjective. This makes it difficult to make a definitive diagnosis for the patient. Recently, deep learning methods have shown promise for depression detection. However, current methods tend to focus solely on the connections within or between audio signals, leading to limitations in the model’s ability to recognize depression-related cues in audio signals and affecting its classification performance. To address these limitations, we propose a graph neural network approach for depression recognition that incorporates potential connections within and between audio signals. Specifically, we first use a gated recurrent unit (GRU) to extract time-series information between frame-level features of audio signals. We then construct two graph neural network modules sequentially to explore the potential connections within and between audio signals. The first graph network module constructs a graph using the frame-level features of each audio sample as nodes. The output is obtained as a graph-embedded feature vector representation after the graph convolution layers. Subsequently, the output graph embedding feature vector representation of the first graph network model is used as the nodes of the graph to construct the second graph network. The internal relationship between audio signals is encoded by the property of node neighborhood information propagation. In addition, we use a pre-trained emotion recognition network to extract emotional features that are highly correlated with depression. By further strengthening the connection weights among nodes in the second graph network through a self-attention mechanism, relevant cues are provided for the model to complete depression detection from audio signals. We conducted extensive experiments on three depression datasets, including DAIC-WOZ, MODMA, and D-Vlog. The proposed model achieves better results on several performance evaluation metrics such as accuracy, F1-score, precision, and recall compared to all the compared algorithms, validating its effectiveness.

A novel EEG-based graph convolution network for depression detection: Incorporating secondary subject partitioning and attention mechanism

Electroencephalography (EEG) is capable of capturing the evocative neural information within the brain. As a result, it has been increasingly used for identifying neurological disorders, such as depression. In recent years, researchers have proposed deep-learning models for EEG-based depression detection and achieved good results. However, there are still some limitations in these models, as the varying importance across different EEG channels and the varying importance of different features within the same channel for each subject have not been adequately addressed. Furthermore, the variations in EEG data distributions among different subjects have not been fully considered, thereby compromising the universality of the model in cross-subject tasks. To address the aforementioned problems, we propose a model with a secondary subject partitioning and attention mechanism based on a graph convolution network (GCN). First, we present an attention module that can simultaneously concentrate on multiple channels with different features within each channel. Second, domain generalization based on adversarial training is added to the model, and a secondary subject partitioning method is proposed to group subjects with similar data distributions into the same domain with a shared domain label. This effectively reduces the number of domain labels and increases the data volume in each domain, thereby enhancing the domain generalization performance. Finally, in the depression recognition task, the improved domain generalization and attention modules collaborate to capture subject-invariant features. Prediction accuracies of 92.87% and 83.17% are respectively achieved on two public datasets, outperforming the state-of-the-art baseline models. Moreover, extensive ablation experiments further validate the effectiveness of each module in the model.

Mental Health First Aid training for China: protocol for a randomised controlled trial

ABSTRACT Objective This study involves a cluster randomised controlled trial (cRCT) of the effectiveness of Mental Health First Aid (MHFA) Training for China. Method A two-arm wait- list cRCT will be conducted in 6 settings: 2 health services, 2 workplaces and 2 community settings in Shanghai. We aim to include a total of 120 participants. The cluster level will be defined as the setting. Intervention settings will be paired according to type and one of each pair will be randomly allocated to the intervention (the MHFA Training for China) or the wait-list control group, with a 1:1 allocation. Participants will be asked to complete questionnaires at baseline (T1) and 1 month (T2) and 7 months after completing the training (T3). Participants in the control group will be asked to complete questionnaires at corresponding times. The primary outcome will be intended support towards someone experiencing a mental health problem or in a mental health crisis. Secondary outcome measures will include recognition of depression in a vignette; beliefs about the helpfulness of interventions; helping actions; confidence in providing mental health first aid; and stigmatising attitudes towards a person with a mental health problem. Results Findings will demonstrate whether the culturally adapted MHFA Training for China can enhance intended support, knowledge, attitudes and supportive actions towards someone developing a mental health problem or in a mental health crisis. Discussion Findings could provide evidence for disseminating MHFA Training for China more widely, potentially improving support for people experiencing mental health problems and benefiting their wellbeing. Trial registration The trial is registered with the International Standard Randomised Controlled Trial Number’ (ISRCTN) Registry. Date 18 February 2023, ISRCTN21096283.

Depression Recognition Using Remote Photoplethysmography From Facial Videos

Depression is a mental illness that may be harmful to an individual's health. The detection of mental health disorders in the early stages and a precise diagnosis are critical to avoid social, physiological, or psychological side effects. This work analyzes physiological signals to observe if different depressive states have a noticeable impact on the blood volume pulse (BVP) and the heart rate variability (HRV) response. Although typically, HRV features are calculated from biosignals obtained with contact-based sensors such as wearables, we propose instead a novel scheme that directly extracts them from facial videos, just based on visual information, removing the need for any contact-based device. Our solution is based on a pipeline that is able to extract complete remote photoplethysmography signals (rPPG) in a fully unsupervised manner. We use these rPPG signals to calculate over 60 statistical, geometrical, and physiological features that are further used to train several machine learning regressors to recognize different levels of depression. Experiments on two benchmark datasets indicate that this approach offers comparable results to other audiovisual modalities based on voice or facial expression, potentially complementing them. In addition, the results achieved for the proposed method show promising and solid performance that outperforms hand-engineered methods and is comparable to deep learning-based approaches.

Depression recognition from facial videos: Preprocessing and scheduling choices hide the architectural contributions

AbstractDeep learning models have been widely applied in video‐based depression detection. It is observed that the diversity of preprocessing, data augmentation, and optimization techniques makes it difficult to fairly compare model architectures. In this study, the typical ResNet‐50 model is enhanced by using specific face alignment methods, improved data augmentation, optimization, and scheduling techniques. The extensive experiments on two popular benchmark datasets (AVEC2013 and AVEC2014) obtained competitive results, compared to sophisticated spatio‐temporal models for single streams. Moreover, the score‐level fusion approach based on two texture streams outperformed the state‐of‐the‐art methods. It achieved mean square errors of 5.82 and 5.50 on AVEC2013 and AVEC2014, respectively. These findings suggest that the preprocessing and training configurations result in noticeable improvements, which have been originally attributed to the network architectures.

AMGCN-L: an adaptive multi-time-window graph convolutional network with long-short-term memory for depression detection

Objective. Depression is a common chronic mental disorder characterized by high rates of prevalence, recurrence, suicide, and disability as well as heavy disease burden. An accurate diagnosis of depression is a prerequisite for treatment. However, existing questionnaire-based diagnostic methods are limited by the innate subjectivity of medical practitioners and subjects. In the search for a more objective diagnostic methods for depression, researchers have recently started to use deep learning approaches. Approach. In this work, a deep-learning network, named adaptively multi-time-window graph convolutional network (GCN) with long-short-term memory (LSTM) (i.e. AMGCN-L), is proposed. This network can automatically categorize depressed and non-depressed people by testing for the existence of inherent brain functional connectivity and spatiotemporal features contained in electroencephalogram (EEG) signals. AMGCN-L is mainly composed of two sub-networks: the first sub-network is an adaptive multi-time-window graph generation block with which adjacency matrices that contain brain functional connectivity on different time periods are adaptively designed. The second sub-network consists of GCN and LSTM, which are used to fully extract the innate spatial and temporal features of EEG signals, respectively. Main results. Two public datasets, namely the patient repository for EEG data and computational tools, and the multi-modal open dataset for mental-disorder analysis, were used to test the performance of the proposed network; the depression recognition accuracies achieved in both datasets (using tenfold cross-validation) were 90.38% and 90.57%, respectively. Significance. This work demonstrates that GCN and LSTM have eminent effects on spatial and temporal feature extraction, respectively, suggesting that the exploration of brain connectivity and the exploitation of spatiotemporal features benefit the detection of depression. Moreover, the proposed method provides effective support and supplement for the detection of clinical depression and later treatment procedures.

FBANet: Transfer Learning for Depression Recognition Using a Feature-Enhanced Bi-Level Attention Network.

The House-Tree-Person (HTP) sketch test is a psychological analysis technique designed to assess the mental health status of test subjects. Nowadays, there are mature methods for the recognition of depression using the HTP sketch test. However, existing works primarily rely on manual analysis of drawing features, which has the drawbacks of strong subjectivity and low automation. Only a small number of works automatically recognize depression using machine learning and deep learning methods, but their complex data preprocessing pipelines and multi-stage computational processes indicate a relatively low level of automation. To overcome the above issues, we present a novel deep learning-based one-stage approach for depression recognition in HTP sketches, which has a simple data preprocessing pipeline and calculation process with a high accuracy rate. In terms of data, we use a hand-drawn HTP sketch dataset, which contains drawings of normal people and patients with depression. In the model aspect, we design a novel network called Feature-Enhanced Bi-Level Attention Network (FBANet), which contains feature enhancement and bi-level attention modules. Due to the limited size of the collected data, transfer learning is employed, where the model is pre-trained on a large-scale sketch dataset and fine-tuned on the HTP sketch dataset. On the HTP sketch dataset, utilizing cross-validation, FBANet achieves a maximum accuracy of 99.07% on the validation dataset, with an average accuracy of 97.71%, outperforming traditional classification models and previous works. In summary, the proposed FBANet, after pre-training, demonstrates superior performance on the HTP sketch dataset and is expected to be a method for the auxiliary diagnosis of depression.

Spatial–Temporal Attention Network for Depression Recognition from facial videos

Recent studies focus on the utilization of deep learning approaches to recognize depression from facial videos. However, these approaches have been hindered by their limited performance, which can be attributed to the inadequate consideration of global spatial–temporal relationships in significant local regions within faces. In this paper, we propose Spatial–Temporal Attention Depression Recognition Network (STA-DRN) for depression recognition to enhance feature extraction and increase the relevance of depression recognition by capturing the global and local spatial–temporal information. Our proposed approach includes a novel Spatial–Temporal Attention (STA) mechanism, which generates spatial and temporal attention vectors to capture the global and local spatial–temporal relationships of features. To the best of our knowledge, this is the first attempt to incorporate pixel-wise STA mechanisms for depression recognition based on 3D video analysis. Additionally, we propose an attention vector-wise fusion strategy in the STA module, which combines information from both spatial and temporal domains. We then design the STA-DRN by stacking STA modules ResNet-style. The experimental results on AVEC 2013 and AVEC 2014 show that our method achieves competitive performance, with mean absolute error/root mean square error (MAE/RMSE) scores of 6.15/7.98 and 6.00/7.75, respectively. Moreover, visualization analysis demonstrates that the STA-DRN responds significantly in specific locations related to depression. The code is available at: https://github.com/divertingPan/STA-DRN.

Multitask learning for recognizing stress and depression in social media

Stress and depression are prevalent nowadays across people of all ages due to the quick paces of life. People use social media to express their feelings. Thus, social media constitute a valuable form of information for the early recognition of stress and depression. Although many research works have been introduced targeting the early recognition of stress and depression, there are still limitations. There have been proposed multi-task learning settings, which use depression and emotion (or figurative language) as the primary and auxiliary tasks respectively. However, although stress is inextricably linked with depression, researchers face these two tasks as two separate tasks. To address these limitations, we present the first study, which exploits two different datasets collected under different conditions, and introduce two multitask learning frameworks, which use depression and stress as the main and auxiliary tasks respectively. Specifically, we use a depression dataset and a stressful dataset including stressful posts from ten subreddits of five domains. In terms of the first approach, each post passes through a shared BERT layer, which is updated by both tasks. Next, two separate BERT encoder layers are exploited, which are updated by each task separately. Regarding the second approach, it consists of shared and task-specific layers weighted by attention fusion networks. We conduct a series of experiments and compare our approaches with existing research initiatives, single-task learning, and transfer learning. Experiments show multiple advantages of our approaches over state-of-the-art ones.

A Depression Recognition Method Based on the Alteration of Video Temporal Angle Features

In recent years, significant progress has been made in the auxiliary diagnosis system for depression. However, most of the research has focused on combining features from multiple modes to enhance classification accuracy. This approach results in increased space-time overhead and feature synchronization problems. To address this issue, this paper presents a single-modal framework for detecting depression based on changes in facial expressions. Firstly, we propose a robust method for extracting angle features from facial landmarks. Theoretical evidence is provided to demonstrate the translation and rotation invariance of these features. Additionally, we introduce a flip correction method to mitigate angle deviations caused by head flips. The proposed method not only preserves the spatial topological relationship of facial landmarks, but also maintains the temporal correlation between frames preceding and following the facial landmarks. Finally, the GhostNet network is employed for depression detection, and the effectiveness of various modal data is compared. In the depression binary classification task using the DAIC-WOZ dataset, our proposed framework significantly improves the classification performance, achieving an F1 value of 0.80 for depression detection. Experimental results demonstrate that our method outperforms other existing depression detection models based on a single modality.

Correlation between depression and perceived stigma among people living with epilepsy

BackgroundEpilepsy is characterized by recurrent seizures that happen in men and women of all ages. Comorbid depression is common with epilepsy due to its social stigma. This study aimed to describe the correlation between depression and perceived stigma among people living with epilepsy. MethodsIt was a cross-sectional descriptive study conducted with 96 people with epilepsy using the convenience sampling method. Fisher’s exact test was used to analyze the association between sociodemographic data, levels of depression, and perceived stigma. Pearson’s correlation coefficient was used to analyze the relationship between depression and perceived stigma. ResultsTwenty-three percent of respondents were found to be depressed, in that 16.7% were mildly depressed, 4.2% were moderately depressed, and 2.1% were severely depressed. Perceived stigma was found in 85.5%, of which 74% were moderately stigmatized and 11.5% were highly stigmatized. This study revealed a significant positive relationship between depression and perceived stigma (r = 0.21) at the p = 0.04 level. ConclusionIt highlights the correlation between perceived stigma and depression; if the patients felt stigmatized by epilepsy, they had a higher chance of having depression. Healthcare providers need to strengthen awareness in society for stigma reduction and early recognition of comorbid depression.

Depression Recognition From EEG Signals Using an Adaptive Channel Fusion Method Via Improved Focal Loss.

Depression is a serious and common psychiatric disease characterized by emotional and cognitive dysfunction. In addition, the rates of clinical diagnosis and treatment for depression are low. Therefore, the accurate recognition of depression is important for its effective treatment. Electroencephalogram (EEG) signals, which can objectively reflect the inner states of human brains, are regarded as promising physiological tools that can enable effective and efficient clinical depression diagnosis and recognition. However, one of the challenges regarding EEG-based depression recognition involves sufficiently optimizing the spatial information derived from the multichannel space of EEG signals. Consequently, we propose an adaptive channel fusion method via improved focal loss (FL) functions for depression recognition based on EEG signals to effectively address this challenge. In this method, we propose two improved FL functions that can enhance the separability of hard examples by upweighting their losses as optimization objectives and can optimize the channel weights by a proposed adaptive channel fusion framework. The experimental results obtained on two EEG datasets show that the developed channel fusion method can achieve improved classification performance. The learned channel weights include the individual characteristics of each EEG epoch, which can effectively optimize the spatial information of each EEG epoch via the channel fusion method. In addition, the proposed method performs better than the state-of-the-art channel fusion methods.

Clustering-Fusion Feature Selection Method in Identifying Major Depressive Disorder Based on Resting State EEG Signals

Depression is a heterogeneous syndrome with certain individual differences among subjects. Exploring a feature selection method that can effectively mine the commonness intra-groups and the differences inter-groups in depression recognition is therefore of great significance. This study proposed a new clustering-fusion feature selection method. Hierarchical clustering (HC) algorithm was used to capture the heterogeneity distribution of subjects. Average and similarity network fusion (SNF) algorithms were adopted to characterize the brain network atlas of different populations. Differences analysis was also utilized to obtain the features with discriminant performance. Experiments showed that compared with traditional feature selection methods, HCSNF method yielded the optimal classification results of depression recognition in both sensor and source layers of electroencephalography (EEG) data. Especially in the beta band of EEG data at sensor layer, the classification performance was improved by more than 6%. Moreover, the long-distance connections between parietal-occipital lobe and other brain regions not only have high discriminative power, but also significantly correlate with depressive symptoms, indicating the important role of these features in depression recognition. Therefore, this study may provide methodological guidance for the discovery of reproducible electrophysiological biomarkers and new insights into common neuropathological mechanisms of heterogeneous depression diseases.

LQGDNet: A Local Quaternion and Global Deep Network for Facial Depression Recognition

Recent visual-based depression recognition methods mostly use hand-crafted features with information lost in color channels, or deep network features with a limited performance from the finite data. In this paper, we propose a method called Local Quaternion and Global Deep Network (LQGDNet) which can combine advantages from hand-crafted and deep features. Specifically, the Quaternion XOR Asymmetrical Regional Local Gradient Coding (XOR-AR-LGC) is firstly designed, which encodes the facial images with local textures in the quaternion domain to keep the dependence of color channels, and integrated into the Quaternion Feature Extractor (QFE). To the best of our knowledge, it is the first attempt to use a quaternion-based method for facial depression recognition. Second, we design the Local Quaternion Representation Module (LQRM) composed of Local Deep Feature Extractor (LDFE) and QFE to output local quaternion facial features. Third, global deep facial features are encoded from the Global Deep Representation Module (GDRM) with the deep convolutional neural network. Finally, the LQGDNet integrates LQRM and GDRM with the local quaternion and global deep features and predicts the depression score. The experimental results on AVEC 2013 and AVEC 2014 show the superiority of our method compared to the state-of-the-art approaches.