Recognition Of Depression Research Articles

Detection of Major Depressive Disorder Using Genetic Algorithm for Features from EEG Signals

Introduction: Depression is a widespread mental disorder that significantly impacts individuals' well-being and quality of life. In the previous few years, researchers have looked into the potential of electroencephalogram signals in detecting and diagnosing depression. The present study investigates an approach for depression detection using a genetic algorithm to optimize the selection of discrete wavelet transform features from EEG signals. The proposed method involves decomposing EEG data into seven sub-bands using the DWT, extracting relevant statistical, geometric, and physiological features, and then employing a genetic algorithm to identify the most informative features for depression recognition. The SVM classifier achieved the highest overall accuracy of 91.73%, a sensitivity of 91.01%, a recall of 92.18%, and an F1-score of 91.45%, outperforming the other models. Experimental results indicate that the proposed approach outperforms hand-engineered methods and highlighting its potential as a complementary tool for depression diagnosis.

Sparse discriminant manifold projections for automatic depression recognition

In recent years, depression has become an increasingly serious problem globally. Previous research have shown that EEG-based depression recognition is a promising technique to serve as auxiliary diagnosis methods that provide assistance to clinicians. Typically, in clinical studies, due to the multichannel nature of EEG, the extracted features usually are high-dimensional and contain many redundant information. Therefore, it is necessary to perform dimensionality reduction before classification to improve the performance of machine learning algorithms. However,existing dimensionality reduction techniques do not design the objective function based on the characteristics of EEG signal and the goal of depression recognition, so they are less suitable for dimensionality reduction of EEG features. To solve this problem, in this paper we propose a novel dimensionality reduction technique called sparse discriminant manifold projections(SDMP) for depression recognition. Specifically, the use of the ℓ2-norm instead of the squared ℓ2-norm as a similarity measure in the objective function reduces sensitivity to noise and outliers. Moreover, the local geometric structure and global discriminative properties of data are integrated, which makes the extracted features more discriminative. Finally, the ℓ2,1-norm regularization is introduced to achieve feature selection. Furthermore, The formulation is extended to the ℓ2,p-norm regularization case, which is more likely to offer better sparsity when 0<p<1. Extensive experiments on EEG data show that the SDMP achieves the competitive performance compared with other state-of-the-art dimensionality reduction methods. It also shows the practical application value of our method in detecting depression.

Embracing complexity in psychiatry-from reductionistic to systems approaches.

The understanding and treatment of psychiatric disorders present unique challenges due to these conditions' multifaceted nature, comprising dynamic interactions between biological, psychological, social, and environmental factors. Traditional reductionistic approaches often simplify these conditions into linear cause-and-effect relationships, overlooking the complexity and interconnectedness inherent in psychiatric disorders. Advances in complex systems approaches provide a comprehensive framework to capture and quantify the non-linear and emergent properties of psychiatric disorders. This Personal View emphasises the importance of identifying rules for generative models that govern brain and behaviour over time, which might contribute to personalised assessments and interventions for psychiatric disorders. For instance, mood fluctuations in bipolar disorder can be understood through dynamical systems modelling, which identifies modifiable parameters, such as circadian disruption, that can be addressed through targeted therapies such as light therapy. Similarly, recognition of depression as an emergent property arising from complex interactions highlights the need for integrated treatment strategies that enhance adaptive reactions in the individual. A framework for quantifying multilevel interactions and network dynamics can help researchers and clinicians to understand the interplay between neural circuits, behaviours, and social contexts. Probabilistic models and self-organisation concepts contribute to building concrete dynamical systems models of mental disorders, facilitating early identification of risk states and promoting resilience through adaptive interventions delivered with optimal timing. Embracing these complex systems approaches in psychiatry could capture the true nature of psychiatric disorders as properties of a dynamic complex system and not the manifestation of any lesion or insult. This line of thinking might improve diagnosis and treatment, offering new hope for individuals affected by psychiatric conditions and paving the way for more effective, personalised mental health care.

Multilevel hybrid handcrafted feature extraction based depression recognition method using speech

Background and purposeDiagnosis of depression is based on tests performed by psychiatrists and information provided by patients or their relatives. In the field of machine learning (ML), numerous models have been devised to detect depression automatically through the analysis of speech audio signals. While deep learning approaches often achieve superior classification accuracy, they are notably resource-intensive. This research introduces an innovative, multilevel hybrid feature extraction-based classification model, specifically designed for depression detection, which exhibits reduced time complexity. Materials and methodsMODMA dataset consisting of 29 healthy and 23 Major depressive disorder audio signals was used. The constructed model architecture integrates multilevel hybrid feature extraction, iterative feature selection, and classification processes. During the Hybrid Handcrafted Feature (HHF) generation stage, a combination of textural and statistical methods was employed to extract low-level features from speech audio signals. To enhance this process for high-level feature creation, a Multilevel Discrete Wavelet Transform (MDWT) was applied. This technique produced wavelet subbands, which were then input into the hybrid feature extractor, enabling the extraction of both high and low-level features. For the selection of the most pertinent features from these extracted vectors, Iterative Neighborhood Component Analysis (INCA) was utilized. Finally, in the classification phase, a one-dimensional nearest neighbor classifier, augmented with ten-fold cross-validation, was implemented to achieve detailed, results. ResultsThe HHF-based speech audio signal classification model attained excellent performance, with the 94.63 % classification accuracy. ConclusionsThe findings validate the remarkable proficiency of the introduced HHF-based model in depression classification, underscoring its computational efficiency.

TCEDN: A Lightweight Time-Context Enhanced Depression Detection Network.

The automatic video recognition of depression is becoming increasingly important in clinical applications. However, traditional depression recognition models still face challenges in practical applications, such as high computational costs, the poor application effectiveness of facial movement features, and spatial feature degradation due to model stitching. To overcome these challenges, this work proposes a lightweight Time-Context Enhanced Depression Detection Network (TCEDN). We first use attention-weighted blocks to aggregate and enhance video frame-level features, easing the model's computational workload. Next, by integrating the temporal and spatial changes of video raw features and facial movement features in a self-learning weight manner, we enhance the precision of depression detection. Finally, a fusion network of 3-Dimensional Convolutional Neural Network (3D-CNN) and Convolutional Long Short-Term Memory Network (ConvLSTM) is constructed to minimize spatial feature loss by avoiding feature flattening and to achieve depression score prediction. Tests on the AVEC2013 and AVEC2014 datasets reveal that our approach yields results on par with state-of-the-art techniques for detecting depression using video analysis. Additionally, our method has significantly lower computational complexity than mainstream methods.

Depression recognition using high-order generalized multilayer brain functional network fused with EEG multi-domain information

Major Depressive Disorder (MDD) is a serious and highly heterogeneous psychological disorder. According to the network hypothesis, depression originates from abnormal neural network information processing, typically resulting in aberrant changes in the topological structure of the brain’s functional network. Recent evidence further reveals that depression involves dynamic changes related to both within- and cross-frequency coupling. Therefore, we utilize second-order tensor expansion to integrate frequency- and time-varying multilayer brain functional networks based on node sharing, thus propose a generalized multilayer brain functional network (GMBFN) incorporating multi-domain information. Concurrently, we derive global and local topological properties from both the frequency and temporal domains to characterize the novel network structure. To uncover more reliable biomarkers and explore various coupling features that can assess the interaction between signals from different perspectives, we conduct research in two datasets employing four sets of within- and cross-frequency coupling. Leveraging the novel multi-domain high-order GMBFNs, abnormalities of information integration abilities in patients with MDD are observed, particularly in the theta-band and overall temporal-domain. Through the fusion of topological properties across both domains with multiple classifiers, the alpha-band can serve as a potential biomarker for depression identification. More importantly, the combination of global topological properties from both domains, on average, enhances the classification performance for identifying patients with MDD by 5.18% compared to using just one domain. This study presents a systematic framework for comprehending the aberrant mechanisms of MDD from multiple perspectives, offering significant value for clinical applications aimed at assisting in depression diagnosis and intervention.

Effect of laughing therapy on level of depression among elderly

The early recognition and management of depression in the elderly in old age homes using the healthcare resources help to return them to a happier and more fulfilling life. This study investigated the effect of laughing therapy on level of depression among the elderly in selected old age homes in Thiruvananthapuram. The objectives of the study were to assess the level of depression among elderly and to find out the effect of laughing therapy on level of depression among elderly. The third objective was to find out the association of pre-test level of depression among elderly and selected demographic variables. The study was based on J.W.Kenny’s Open System Model. The research design of this study is Quasi experimental two group pre-test-post-test design. The sample consisted of 60 elderly residing in old age homes Thiruvananthapuram district. Purposive sampling was used. The instruments used in this study were the Modified Yesavage Geriatric Depression Scale and demographic proforma. The participants in the treatment group involved in laughing therapy for 30 minutes each day for 7 consecutive days and those in the control group were monitored for depression. A pre-test was given before administering the laughing therapy and a post-test was performed after 1 week. The findings revealed that in experimental group 60% had mild depression and 40% had moderate depression and in control group 46.7% had mild depression and 53.3% had moderate depression. The laughing therapy given to elderly residing in old age homes had significantly reduced their level of depression (p &lt; 0.01) and also there was significant association between level of depression and gender and educational status. Laughing therapy is an innovative option to promote a healthy environment for elderly.

KWHO-CNN: A Hybrid Metaheuristic Algorithm Based Optimzed Attention-Driven CNN for Automatic Clinical Depression Recognition

KWHO-CNN: A Hybrid Metaheuristic Algorithm Based Optimzed Attention-Driven CNN for Automatic Clinical Depression Recognition

Research on Depression Recognition Based on University Students’ Facial Expressions and Actions with the Assistance of Artificial Intelligence

As artificial intelligence (AI) technology advances, its application in the field of psychology has witnessed significant advancements. In this paper, with the assistance of AI, 80 university students with depression and 80 university students with normal psychology were selected as the subjects. The facial expression feature data were extracted through OpenFace, and the action feature data were extracted based on a Kinect camera. Then, the convolutional neural network-long short-term memory (CNN-LSTM) and temporal convolutional neural network (TCN) approaches were designed for recognition. Finally, a weighted fusion recognition method was proposed. The results showed that compared with the support vector machine, back-propagation neural network, and other approaches, the CNN-LSTM and TCN methods showed better performance in the recognition of single feature data, and the accuracy reached 0.781 and 0.769, respectively. After weighted fusion, the accuracy reached the highest at 0.875. The results verify that the methods designed in this paper are effective in identifying depressive emotions through facial expressions and actions among university students and have the potential for practical application.

Knowledge of and attitude towards depression among urban and rural residents in Beijing: a cross-sectional study

ObjectivesTo compare the level of knowledge of depression, recognition ability and attitudes towards depression among urban and rural residents in Beijing.DesignA cross-sectional study.SettingSix districts in Beijing, China, 2021.ParticipantsA total of...

HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals.

Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehensive consideration of depression's pathogenesis, leading to limited neuroscientific interpretability. To address these issues, we propose a hemisphere asymmetry network (HEMAsNet) inspired by the brain for depression recognition from EEG signals. HEMAsNet employs a combination of multi-scale Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) blocks to extract temporal features from both hemispheres of the brain. Moreover, the model introduces a unique 'Callosum-like' block, inspired by the corpus callosum's pivotal role in facilitating inter-hemispheric information transfer within the brain. This block enhances information exchange between hemispheres, potentially improving depression recognition accuracy. To validate the performance of HEMAsNet, we first confirmed the asymmetric features of frontal lobe EEG in the MODMA dataset. Subsequently, our method achieved a depression recognition accuracy of 0.8067, indicating its effectiveness in increasing classification performance. Furthermore, we conducted a comprehensive investigation from spatial and frequency perspectives, demonstrating HEMAsNet's innovation in explaining model decisions. The advantages of HEMAsNet lie in its ability to achieve more accurate and interpretable recognition of depression through the simulation of physiological processes, integration of spatial information, and incorporation of the Callosum-like block.

Dual‐task enhanced global–local temporal–spatial network for depression recognition from facial videos

SummaryIn previous studies on facial video depression recognition, although convolutional neural network (CNN) has become a mainstream method, its performance still has room for improvement due to the insufficient extraction of global and local information and the neglect of the correlation of temporal and spatial information. This paper proposes a novel dual‐task enhanced global–local temporal–spatial network (DTE‐GLTS) to enhance the extraction capability of global and local features and deepen the analysis of temporal–spatial information correlation. We design a dual‐task learning mode that utilizes the data‐efficient image transformer (Deit) as the main body to learn the global features of video sequences and guides Deit to learn local features with the pre‐trained temporal–spatial fusion network (TSF). In addition, we propose the TSF mechanism to more effectively fuse temporal–spatial information in video sequences, strengthen the correlation between frames and pixels, and embed it in Resnet to form the TSF network. To the best of our knowledge, this is the first application of Deit and dual‐task learning mode in the field of facial video depression recognition. 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 6.06/7.73 and 5.91/7.68, respectively, while significantly reducing the number of parameters.

Association of Depression and Cognitive Performance in US Older Adults: A Secondary Analysis of Cross-Sectional Data Using NHANES 2013–2014

Introduction: Depression has been associated with cognitive performance, but whether sociodemographic and clinical characteristics might influence this association is not well elaborated. This study aimed to further explore this relationship in older adults. Methods: This cross-sectional study is based on data from the National Health and Nutrition Examination Survey (NHANES) 2013–2014. A total of 1,433 individuals with complete information on depressive symptoms and cognitive function variables were included in this study. Patient Health Questionnaire 9 (PHQ-9) score ≥10 as the cutoff to identify cases of depression in our study. We defined poor cognitive performance as a composite cognitive score <47. Logistic regression models were used to examine the association of depression with cognitive performance (model 1). We progressively adjusted the covariates as confounders (model 2: model 1 + age, and gender; model 3: model 2 + race, education level, family income, drinking, and smoking; model 4: model 3 + overweight, arthritis, hyperlipidemia, diabetes, hypertension, heart failure, coronary heart disease, heart attack, stroke, and cancer). We then conducted subgroup, interaction, and restricted cubic spline (RCS) analyses to examine this association. Results: The prevalence of poor cognitive performance was 36.6% (53/145) in the depression group and 14.1% (182/1,288) in the non-depression group. In the fully adjusted model, depression was significantly associated with poor cognitive performance (adjusted odds ratio: 2.25; 95% confidence interval: 1.31–3.81). The results were robust to sensitivity analyses. Gender and education level may modify the association between depression and poor cognitive performance. RCS analysis revealed that the PHQ-9 score was related to poor cognitive performance in a nonlinear manner (p for nonlinearity <0.001), and exhibited a J-shaped curve. Conclusion: Depression is associated with poor cognitive performance in US older adults. Early recognition and treatment of depression may be potential intervention strategies to protect cognitive health.

Improving Parkinson's Disease Care through Systematic Screening for Depression.

Depression is common in Parkinson's disease (PD) but is underrecognized clinically. Although systematic screening is a recommended strategy to improve depression recognition in primary care practice, it has not been widely used in PD care. The 15-item Geriatric Depression Scale (GDS-15) was implemented at 5 movement disorders clinics to screen PD patients. Sites developed processes suited to their clinical workflow. Qualitative interviews with clinicians and patients provided information on feasibility, acceptability, and perceived utility. Prior to implementation, depression screening was recorded in 12% using a formal instrument; 64% were screened informally by clinical interview, and no screening was recorded in 24%. Of 1406 patients seen for follow-up care during the implementation period, 88% were screened, 59% using the GDS-15 (self-administered in 51% and interviewer administered in 8%), a nearly 5-fold increase in formal screening. Lack of clinician or staff time and inability to provide the GDS-15 to the patient ahead of the visit were the most commonly cited reasons for lack of screening using the GDS-15; 378 (45%) patients completing the GDS-15 screened positive for depression, and 137 were enrolled for a 12-month prospective follow-up. Mean GDS-15 scores improved from 8.8 to 7.0 (P < 0.0001) and the 39-item Parkinson's Disease Questionnaire emotional subscore from 42.2 to 36.7 (P = 0.0007). Depression screening in PD using a formal instrument can be achieved at much higher levels than is currently practiced, but there are barriers to implementing this in clinical practice. An individual site-specific process is necessary to optimize screening rates.

Models for depression recognition and efficacy assessment based on clinical and sequencing data

Major depression is a complex psychiatric disorder that includes genetic, neurological, and cognitive factors. Early detection and intervention can prevent progression, and help select the best treatment. Traditional clinical diagnosis tends to be subjective and misdiagnosed. Based on this, this study leverages clinical scale assessments and sequencing data to construct disease prediction models. Firstly, data undergoes preprocessing involving normalization and other requisite procedures. Feature engineering is then applied to curate subsets of features, culminating in the construction of a model through the implementation of machine learning and deep learning algorithms. In this study, 18 features with significant differences between patients and healthy controls were selected. The depression recognition model was constructed by deep learning with an accuracy of 87.26 % and an AUC of 91.56 %, which can effectively distinguish patients with depression from healthy controls. In addition, 33 features selected by recursive feature elimination method were used to construct a prognostic effect model of patients after 2 weeks of treatment, with an accuracy of 75.94 % and an AUC of 83.33 %. The results show that the deep learning algorithm based on clinical and sequencing data has good accuracy and provides an objective and accurate method for the diagnosis and pharmacodynamic prediction of depression. Furthermore, the selected differential features can serve as candidate biomarkers to provide valuable clues for diagnosis and efficacy prediction.

Risk factors and methods in suicides of elderly patients connected to mental health services from 1999-2024.

Suicide prevention is an important aspect of psychiatric care, with older men being a population identified at especially high suicide risk and a recent increase in suicides among older women. Using data collected by the region's quality assurance team, we examined all suicide deaths occurring between March 1999 and February 2024 in patients aged 60 years or older who were connected to the region's Addiction and Mental Health Program at the time of death. Data were analyzed to describe which factors were most commonly identified in suicides in older adults receiving mental healthcare. We also compared male and female cases to determine whether certain factors were more commonly observed in one gender. We identified 48 cases of suicide occurring in patients aged 60 or over. 60% of suicides occurred in males. Overdose and hanging were the most common suicide methods used, and all suicides occurring on inpatient units occurred via hanging. Depression was the most common diagnosis, and was diagnosed more frequently in suicides of female older adults. A greater proportion of suicides in older women were associated with previous history of suicide attempts. Our findings support many current best practices for suicide prevention in psychiatric care, including minimizing ligatures and anchor points on inpatient settings, assessing for and limiting access to means in individuals at-risk, and assessing suicide risk in hospitalized patients prior to passes and discharge. Recognition and treatment of depression remain important aspects in the treatment of older adults to prevent suicide.

Depression risk recognition based on gait: A benchmark

Recently, depression recognition has received considerable attention. Due to easy acquisition at a distance, gait-based depression recognition can be a useful tool for auxiliary diagnosis and self-help depression risk assessment. Most existing methods use a few hand-craft features for analysis. To further investigate the relationship between depression and gait, we collect a gait-based depression dataset named D-Gait. 27,120 gait sequences of 292 volunteers with informed consent are collected to support the data-driven methodology (the private information is eliminated). Multiple shooting angles and three kinds of clothing are taken into consideration. To the best of our knowledge, it is the first published gait-based depression dataset. Based on this dataset, a gait-based depression risk recognition benchmark is established. We systematically investigate representative methods with skeleton and silhouette data, thereby verifying most psychological research conclusions about the relationship between gait and depression. Besides, more instructive insights are given in our paper, which indicates the significant potential of gait-based depression risk recognition. The benchmark will advance the research on depression, and it can be accessed at https://github.com/BNU-IVC/D-Gait.

Depression recognition using voice-based pre-training model

The early screening of depression is highly beneficial for patients to obtain better diagnosis and treatment. While the effectiveness of utilizing voice data for depression detection has been demonstrated, the issue of insufficient dataset size remains unresolved. Therefore, we propose an artificial intelligence method to effectively identify depression. The wav2vec 2.0 voice-based pre-training model was used as a feature extractor to automatically extract high-quality voice features from raw audio. Additionally, a small fine-tuning network was used as a classification model to output depression classification results. Subsequently, the proposed model was fine-tuned on the DAIC-WOZ dataset and achieved excellent classification results. Notably, the model demonstrated outstanding performance in binary classification, attaining an accuracy of 0.9649 and an RMSE of 0.1875 on the test set. Similarly, impressive results were obtained in multi-classification, with an accuracy of 0.9481 and an RMSE of 0.3810. The wav2vec 2.0 model was first used for depression recognition and showed strong generalization ability. The method is simple, practical, and applicable, which can assist doctors in the early screening of depression.

Depression recognition over fusion of visual and vocal expression using artificial intelligence

&lt;div class="WordSection1"&gt;&lt;p&gt;Depression is a mental illness that usually goes untreated in people and can have catastrophic consequences, including suicidal thoughts. Counselling services are widely available, but because depression is a stigmatized illness, many people who are depressed decide not to seek help. Therefore, it is essential to develop an automated system that can recognize depression in individuals before it worsens. In this study, a novel approach is proposed for identifying depression using a combination of visual and vocal emotions. Long short-term memory (LSTM) is used to assess verbal expressions and convolutional neural networks (CNN) to analyze facial expressions. The proposed system is trained using features of depression from the distress analysis interview corpus (DAIC) dataset and tested on videos of college students with frontal faces. The proposed approach is effective in detecting depression in individuals, with high accuracy and reliability.&lt;/p&gt;&lt;/div&gt;

Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study.

Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition. The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD. Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F1-score. Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method's specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection. This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.