Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry

Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB)

BACKGROUND

The diagnosis of major depressive disorder (MDD) is commonly based on the subjective evaluation by experienced psychiatrists using clinical scales. Hence, it is particularly important to find more objective biomarkers to aid in diagnosis and further treatment. Alpha-band activity (7-13 Hz) is the most prominent component in resting electroencephalogram (EEG), which is also thought to be a potential biomarker. Recent studies have shown the existence of multiple sub-oscillations within the alpha band, with distinct neural underpinnings. However, the specific contribution of these alpha sub-oscillations to the diagnosis and treatment of MDD remains unclear.

METHODS

In this study, we recorded the resting-state EEG from MDD and HC populations in both open and closed-eye state conditions. We also assessed cognitive processing using the MATRICS Consensus Cognitive Battery (MCCB).

RESULTS

We found that the MDD group showed significantly higher power in the high alpha range (10.5-11.5 Hz) and lower power in the low alpha range (7-8.5 Hz) compared to the HC group. Notably, high alpha power in the MDD group is negatively correlated with working memory performance in MCCB, whereas no such correlation was found in the HC group. Furthermore, using five established classification algorithms, we discovered that combining alpha oscillations with MCCB scores as features yielded the highest classification accuracy compared to using EEG or MCCB scores alone.

CONCLUSIONS

Our results demonstrate the potential of sub-oscillations within the alpha frequency band as a potential distinct biomarker. When combined with psychological scales, they may provide guidance relevant for the diagnosis and treatment of MDD.

Exploring risk factors and their differences on suicidal ideation and suicide attempts among depressed adolescents based on decision tree model

BACKGROUND

Suicide has been recognized as a major global public health issue. Depressed adolescents are more prone to experiencing it. We explore risk factors and their differences on suicidal ideation and suicide attempts to further enhance our understanding of suicidal behavior.

METHODS

2343 depressed adolescents aged 12-18 from 9 provinces/cities in China participated in this cross-sectional study. We utilized decision tree model, incorporating 32 factors encompassing participants' suicidal behavior. The feature importance of each factor was measured using Gini coefficients.

RESULTS

The decision tree model demonstrated a good fit with high accuracy (SI = 0.86, SA = 0.85 and F-Score (SI = 0.85, SA = 0.83). The predictive importance of each factor varied between groups with suicidal ideation and with suicide attempts. The most significant risk factor in both groups was depression (SI = 16.7 %, SA = 19.8 %). However, factors such as academic stress (SI = 7.2 %, SA = 1.6 %), hopelessness (SI = 9.1 %, SA = 5.0 %), and age (SI = 7.1 %, SA = 3.2 %) were more closely associated with suicidal ideation than suicide attempts. Factors related to the schooling status (SI = 3.5 %, SA = 10.1 %), total years of education (SI = 2.6 %, SA = 8.6 %), and loneliness (SI = 2.3 %, SA = 7.4 %) were relatively more important in the suicide attempt stage compared to suicidal ideation.

LIMITATIONS

The cross-sectional design limited the ability to capture changes in suicidal behavior among depressed adolescents over time. Possible bias may exist in the measurement of suicidal ideation.

CONCLUSION

The relative importance of each risk factor for suicidal ideation and attempted suicide varies. These findings provide further empirical evidence for understanding suicide behavior. Targeted treatment measures should be taken for different stages of suicide in clinical interventions.

Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records

Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with specific treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia using EEG signals. The principal component analysis (PCA) method was applied to process the obtained data in quantum systems. The data, which were reduced in dimensionality, were transformed into qubit form using various feature maps and provided as input to the Quantum Support Vector Machine (QSVM) algorithm. Thus, the QSVM algorithm was applied using different qubit numbers and different circuits in addition to classical machine learning algorithms. All analyses were conducted in the simulator environment of the IBM Quantum Platform. In the classification of this EEG dataset, it is evident that the QSVM algorithm demonstrated superior performance with a 100% success rate when using Pauli X and Pauli Z feature maps. This study serves as proof that quantum machine learning algorithms can be effectively utilized in the field of healthcare.

Alexithymia modulates emotion concept activation during facial expression processing

Alexithymia is characterized by difficulties in emotional information processing. However, the underlying reasons for emotional processing deficits in alexithymia are not fully understood. The present study aimed to investigate the mechanism underlying emotional deficits in alexithymia. Using the Toronto Alexithymia Scale-20, we recruited college students with high alexithymia (n = 24) or low alexithymia (n = 24) in this study. Participants judged the emotional consistency of facial expressions and contextual sentences while recording their event-related potentials. Behaviorally, the high alexithymia group showed longer response times versus the low alexithymia group in processing facial expressions. The event-related potential results showed that the high alexithymia group had more negative-going N400 amplitudes compared with the low alexithymia group in the incongruent condition. More negative N400 amplitudes are also associated with slower responses to facial expressions. Furthermore, machine learning analyses based on N400 amplitudes could distinguish the high alexithymia group from the low alexithymia group in the incongruent condition. Overall, these findings suggest worse facial emotion perception for the high alexithymia group, potentially due to difficulty in spontaneously activating emotion concepts. Our findings have important implications for the affective science and clinical intervention of alexithymia-related affective disorders.

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

The applied principles of EEG analysis methods in neuroscience and clinical neurology

Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.

Spatial-Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection

In view of the major depressive disorder characteristics such as high mortality as well as high recurrence, it is important to explore an objective and effective detection method for major depressive disorder. Considering the advantages complementary of different machine learning algorithms in information mining process, as well as the fusion complementary of different information, in this study, the spatial-temporal electroencephalography fusion framework using neural network is proposed for major depressive disorder detection. Since electroencephalography is a typical time series signal, we introduce recurrent neural network embedded in long short-term memory unit for extract temporal domain features to solve the problem of long-distance information dependence. To reduce the volume conductor effect, the temporal electroencephalography data are mapping into a spatial brain functional network using phase lag index, then the spatial domain features were extracted from brain functional network using 2D convolutional neural networks. Considering the complementarity between different types of features, the spatial-temporal electroencephalography features are fused to achieve data diversity. The experimental results show that spatial-temporal features fusion can improve the detection accuracy of major depressive disorder with a highest of 96.33%. In addition, our research also found that theta, alpha, and full frequency band in brain regions of left frontal, left central, right temporal are closely related to MDD detection, especially theta frequency band in left frontal region. Only using single-dimension EEG data as decision basis, it is difficult to fully explore the valuable information hidden in the data, which affects the overall detection performance of MDD. Meanwhile, different algorithms have their own advantages for different application scenarios. Ideally, different algorithms should use their respective advantages to jointly address complex problems in engineering fields. To this end, we propose a computer-aided MDD detection framework based on spatial-temporal EEG fusion using neural network, as shown in Fig. 1. The simplified process is as follows: (1) Raw EEG data acquisition and preprocessing. (2) The time series EEG data of each channel are input as recurrent neural network (RNN), and RNN is used to process and extract temporal domain (TD) features. (3) The BFN among different EEG channels is constructed, and CNN is used to process and extract the spatial domain (SD) features of the BFN. (4) Based on the theory of information complementarity, the spatial-temporal information is fused to realize efficient MDD detection. Fig. 1 MDD detection framework based on spatial-temporal EEG fusion.

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning

Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8-30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.

Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine

Depression is a common mental disorder that seriously affects patients' social function and daily life. Its accurate diagnosis remains a big challenge in depression treatment. In this study, we used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) and measured the whole brain EEG signals and forehead hemodynamic signals from 25 depression patients and 30 healthy subjects during the resting state. On one hand, we explored the EEG brain functional network properties, and found that the clustering coefficient and local efficiency of the delta and theta bands in patients were significantly higher than those in normal subjects. On the other hand, we extracted brain network properties, asymmetry, and brain oxygen entropy as alternative features, used a data-driven automated method to select features, and established a support vector machine model for automatic depression classification. The results showed the classification accuracy was 81.8% when using EEG features alone and increased to 92.7% when using hybrid EEG and fNIRS features. The brain network local efficiency in the delta band, hemispheric asymmetry in the theta band and brain oxygen sample entropy features differed significantly between the two groups (p < 0.05) and showed high depression distinguishing ability indicating that they may be effective biological markers for identifying depression. EEG, fNIRS and machine learning constitute an effective method for classifying depression at the individual level.

Machine Learning and Electroencephalogram Signal based Diagnosis of Dipression

Depression is a psychological condition which hampers day to day activity (Thinking, Feeling or Action). The early detection of this illness will help to save many lives because it is now recognized as a global problem which could even lead to suicide. Electroencephalogram (EEG) signals can be used to diagnose depression using machine learning techniques. The dataset studied is public dataset which consists of 30 healthy people and 34 depression patients. The methods used for detection of depression are Decision Tree, Random Forest, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long-Short Term Memory (Bi-LSTM), Gradient Boosting, Extreme Gradient Boosting (XGBoost) along with band power. Among Deep Learning techniques, CNN model got the highest accuracy with 98.13%, specificity of 99%, and sensitivity of 97% using band power features.

TSUnet-CC: Temporal Spectrogram Unet embedding Cross Channel-wise attention mechanism for MDD identification

Automatic detection of major depressive disorder (MDD) with multiple-channel electroencephalography (EEG) signals is of great significance for treatment of the mental diseases. In a U-net network, clear EEG signals are fed to obtain temporal feature tensor through encoder and decoder networks with several convolution operations. Moreover, the clear EEG signals can be converted into multi-scale spectrogram to obtain the rich saliency information and then the spectrogram feature tensor can be extracted by another symmetrical U-net. The temporal and spectrogram feature tensors can provide more comprehensive information, but may also contain redundant information, which may affect the detection of MDD. To deal with such issue, this paper proposed a novel Temporal Spectrogram Unet (TSUnet-CC), which embeds the cross channel-wise attention mechanism for multiple-channel EEGbased MDD identification. We make three novel contributions: 1) multi-scale saliency-encoded spectrogram using Fourierbased approach to capture rich saliency information under different scales, 2) TSUnet network using a symmetrical twostream U-net architecture that learns multiple temporal and spectrogram feature tensors in time and frequency domains, and 3) cross channel-wise block enabling the larger weights of key feature channels that contain MDD information. The leaveone-subject-out experiments show that our proposed TSUnetCC gains high performance with a classification accuracy up to 98.55% and 99.22% in eyes closed and eyes open datasets, which outperformed some state-of-the-art methods and revealed its clinical potential.

Method for Classifying Schizophrenia Patients Based on Machine Learning

Schizophrenia is a chronic and severe mental disorder that affects individuals in various ways, particularly in their ability to perceive, process, and respond to stimuli. This condition has a significant impact on a considerable number of individuals. Consequently, the study, analysis, and characterization of this pathology are of paramount importance. Electroencephalography (EEG) is frequently utilized in the diagnostic assessment of various brain disorders due to its non-intrusiveness, excellent resolution and ease of placement. However, the manual analysis of electroencephalogram (EEG) recordings can be a complex and time-consuming task for healthcare professionals. Therefore, the automated analysis of EEG recordings can help alleviate the burden on doctors and provide valuable insights to support clinical diagnosis. Many studies are working along these lines. In this research paper, the authors propose a machine learning (ML) method based on the eXtreme Gradient Boosting (XGB) algorithm for analyzing EEG signals. The study compares the performance of the proposed XGB-based approach with four other supervised ML systems. According to the results, the proposed XGB-based method demonstrates superior performance, with an AUC value of 0.94 and an accuracy value of 0.94, surpassing the other compared methods. The implemented system exhibits high accuracy and robustness in accurately classifying schizophrenia patients based on EEG recordings. This method holds the potential to be implemented as a valuable complementary tool for clinical use in hospitals, supporting clinicians in their clinical diagnosis of schizophrenia.

A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network

Background

Most patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.

Objective

This study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.

Methods

First, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.

Results

Finally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.

Conclusion

These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.

Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset

Depression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods. In this study, a machine learning-based method for detecting depression among young adults using EEG data recorded by the wireless headset is proposed. For this reason, EEG data has been recorded using an Emotiv Epoc+ headset. A total of 32 young adults participated and the PHQ9 screening tool was used to identify depressed participants. Features such as skewness, kurtosis, variance, Hjorth parameters, Shannon entropy, and Log energy entropy from 1 to 5 sec data filtered at different band frequencies were applied to KNN and SVM classifiers with different kernels. At AB band (8-30 Hz) frequency, 98.43 ± 0.15% accuracy was achieved by extracting Hjorth parameters, Shannon entropy, and Log energy entropy from 5 sec samples with a 5-fold CV using a KNN classifier. And with the same features and classifier overall accuracy = 98.10 ± 0.11, NPV = 0.977, precision = 0.984, sensitivity = 0.984, specificity = 0.976, and F1 score = 0.984 was achieved after splitting the data to 70/30 ratio for training and testing with 5-fold CV. From the findings, it can be concluded that EEG data from an Emotiv headset can be used to detect depression with the proposed method.

Cross-subject classification of depression by using multiparadigm EEG feature fusion

BACKGROUND AND OBJECTIVE

The aim of this study is to solve the non-stationarity and complexity characteristics and huge individual differences in the electroencephalogram (EEG) signals for depression classification.

METHODS

To address those problems, the Lempel-Ziv complexity feature matrices were extracted from the EEG signals under the two paradigms of eyes open and eyes closed in the resting state. Topographical map of brain and statistical analysis were introduced to investigate the significance of eyes open and eyes closed EEG for depression classification. To promote the classification accuracy, feature matrices from the two paradigms were fused. And linear combination and concatenation fusion methods were proposed to further reveal the underlying mechanism of improving classification accuracy. Support vector machine (SVM), K-nearest neighbor, and decision tree classifiers were employed and compared to classify depression under the eyes open, eyes closed and fused paradigm.

RESULTS

The classification results of 10-fold cross-validation showed that the highest average accuracy (86.58%) under a single paradigm was achieved in the eyes-open state. The multiparadigm fusion method of concatenation was better than the linear combination. The best classification result was obtained using multiparadigm feature concatenation under the SVM classifier, yielding an accuracy of 94.03%.

CONCLUSION

The multiparadigm feature fusion method proposed in this paper can effectively improve the accuracy of depression classification. It was proved that eyes open and eyes closed EEG have complementary information, which was benefit for the cross-subject classification of depression. It provides new ideas for depression classification in clinics.

Effect of mindfulness on anxiety and depression in insomnia patients: A systematic review and meta-analysis

Background

As a common clinical symptom, insomnia has a high incidence of combined mental illness and it is also a risk factor for the development of depression, anxiety and suicide. As a new concept in the field of health in recent years, mindfulness therapy can improve insomnia, anxiety and depression, which is a new way to solve such diseases.

Objective

This study aims to systematically evaluate the effects of mindfulness compared with conventional treatment on scores of the Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) in people with insomnia and anxiety-depressive symptoms.

Methods

Articles published before October 2022 were searched from seven databases and included in randomized controlled trials (RCTs) to evaluate mindfulness therapy. The assessment tool of Cochrane bias risk was used to evaluate the methodological quality of the literature. The main outcome indicators were HAMD and HAMA scores, and the secondary outcome indicators were SDS and SAS scores.

Results

Ten randomized controlled trials including 1,058 subjects were systematically evaluated and meta-analyzed in this study. In the main outcome indicators, there was a significant difference between mindfulness therapy and conventional treatment in reducing HAMD score (MD: -3.67, 95% CI: -5.22-2.11, p < 0.01) and HAMA score (MD: -3.23, 95% CI: -3.90-2.57, p < 0.01). In the secondary outcome indicators, mindfulness therapy also showed a significant difference in reducing SDS scores (MD: -6.49, 95% CI: -6.86-6.11, p < 0.01) and SAS scores (MD: -7.97, 95% CI: -9.68-6.27, p < 0.01) compared with conventional treatment.

Conclusion

For the people with insomnia, anxiety and depression, the use of conventional treatment with the addition of 4-12 weeks of mindfulness treatment can significantly improve anxiety and depression symptoms of patients. This is a new diagnosis and treatment idea recommended for insomniacs with or without anxiety and depression symptoms. Due to the methodological defects in the included study and the limited sample size of this paper, more well-designed randomized controlled trials are needed for verification.

The Role of Equilibrium between Free Radicals and Antioxidants in Depression and Bipolar Disorder

Background: Increasing evidence suggests that the presence of oxidative stress and disorders of the antioxidant defense system are involved in a wide range of neuropsychiatric disorders, such as bipolar disorder, schizophrenia and major depression, but the exact mechanism remains unknown. This review focuses on a better appreciation of the contribution of oxidative stress to depression and bipolar disorder. Methods: This review was conducted by extracting information from other research and review studies, as well as other meta-analyses, using two search engines, PubMed and Google Scholar. Results: As far as depression is concerned, there is agreement among researchers on the association between oxidative stress and antioxidants. In bipolar disorder, however, most of them observe strong lipid peroxidation in patients, while regarding antioxidant levels, opinions are divided. Nevertheless, in recent years, it seems that on depression, there are mainly meta-analyses and reviews, rather than research studies, unlike on bipolar disorder. Conclusions: Undoubtedly, this review shows that there is an association among oxidative stress, free radicals and antioxidants in both mental disorders, but further research should be performed on the exact role of oxidative stress in the pathophysiology of these diseases.

A novel EEG-based major depressive disorder detection framework with two-stage feature selection

Background

Major depressive disorder (MDD) is a common mental illness, characterized by persistent depression, sadness, despair, etc., troubling people’s daily life and work seriously.

Methods

In this work, we present a novel automatic MDD detection framework based on EEG signals. First of all, we derive highly MDD-correlated features, calculating the ratio of extracted features from EEG signals at frequency bands between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α. Then, a two-stage feature selection method named PAR is presented with the sequential combination of Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), where the advantages lie in minimizing the feature searching space. Finally, we employ widely used machine learning methods of support vector machine (SVM), logistic regression (LR), and linear regression (LNR) for MDD detection with the merit of feature interpretability.

Results

Experiment results show that our proposed MDD detection framework achieves competitive results. The accuracy and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_{1}$$\end{document}F1 score are up to 0.9895 and 0.9846, respectively. Meanwhile, the regression determination coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document}R2 for MDD severity assessment is up to 0.9479. Compared with existing MDD detection methods with the best accuracy of 0.9840 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document}F1 score of 0.97, our proposed framework achieves the state-of-the-art MDD detection performance.

Conclusions

Development of this MDD detection framework can be potentially deployed into a medical system to aid physicians to screen out MDD patients.

Research on the Method of Depression Detection by Single-Channel Electroencephalography Sensor

Depression is a common mental health illness worldwide that affects our quality of life and ability to work. Although prior research has used EEG signals to increase the accuracy to identify depression, the rates of underdiagnosis remain high, and novel methods are required to identify depression. In this study, we built a model based on single-channel, dry-electrode EEG sensor technology to detect state depression, which measures the intensity of depressive feelings and cognitions at a particular time. To test the accuracy of our model, we compared the results of our model with other commonly used methods for depression diagnosis, including the PHQ-9, Hamilton Depression Rating Scale (HAM-D), and House-Tree-Person (HTP) drawing test, in three different studies. In study 1, we compared the results of our model with PHQ-9 in a sample of 158 senior high students. The results showed that the consistency rate of the two methods was 61.4%. In study 2, the results of our model were compared with HAM-D among 71 adults. We found that the consistency rate of state-depression identification by the two methods was 63.38% when a HAM-D score above 7 was considered depression, while the consistency rate increased to 83.10% when subjects showed at least one depressive symptom (including depressed mood, guilt, suicide, lack of interest, retardation). In study 3, 68 adults participated in the study, and the results revealed that the consistency rate of our model and HTP drawing test was 91.2%. The results showed that our model is an effective means to identify state depression. Our study demonstrates that using our model, people with state depression could be identified in a timely manner and receive interventions or treatments, which may be helpful for the early detection of depression.

Can acupuncture enhance therapeutic effectiveness of antidepressants and reduce adverse drug reactions in patients with depression? A systematic review and meta-analysis

BACKGROUND

Some depressed patients receive acupuncture as an adjunct to their conventional medications.

OBJECTIVE

This review aims to provide evidence on whether acupuncture can enhance the therapeutic effectiveness of antidepressants for treating depression, and explore whether acupuncture can reduce the adverse reactions associated with antidepressants.

SEARCH STRATEGY

English and Chinese databases were searched for randomized controlled trials (RCTs) published until December 1, 2021.

INCLUSION CRITERIA

RCTs with a modified Jadad scale score ≥ 4 were included if they compared a group of participants with depression that received acupuncture combined with antidepressants with a control group that received antidepressants alone.

DATA EXTRACTION AND ANALYSIS

Meta-analysis was performed, and statistical heterogeneity was assessed based on Cochran's Q statistic and its related P-value. Primary outcomes were the reduction in the severity of depression and adverse reactions associated with antidepressants, while secondary outcomes included remission rate, treatment response, social functioning, and change in antidepressant dose. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework was used to evaluate the overall quality of evidence in the included studies.

RESULTS

This review included 16 studies (with a total of 1958 participants). Most studies were at high risk of performance bias and at low or unclear risk of selection bias, detection bias, attrition bias, reporting bias, and other bias. Analysis of the 16 RCTs showed that, compared with antidepressants alone, acupuncture along with antidepressants reduced the Hamilton Depression Rating Scale-17 (HAMD-17) scores (standard mean difference [SMD] -0.44, 95% confidence interval [CI] -0.55 to -0.33, P < 0.01; I² = 14%), Self-rating Depression Scale (SDS) scores (SMD -0.53, 95% CI -0.84 to -0.23, P < 0.01; I² = 79%), and the Side Effect Rating Scale (SERS) scores (SMD -1.11, 95% CI -1.56 to -0.66, P < 0.01; I² = 89%). Compared with antidepressants alone, acupuncture along with antidepressants improved World Health Organization Quality of Life-BREF scores (SMD 0.31, 95% CI 0.18 to 0.44, P < 0.01; I² = 15%), decreased the number of participants who increased their antidepressant dosages (relative risk [RR] 0.32, 95% CI 0.22 to 0.48, P < 0.01; I² = 0%), and resulted in significantly higher remission rates (RR 1.52, 95% CI 1.26 to 1.83, P < 0.01; I² = 0%) and treatment responses (RR 1.35, 95% CI 1.24 to 1.47, P < 0.01; I² = 19%) in terms of HAMD-17 scores. The HAMD-17, SDS and SERS scores were assessed as low quality by GRADE and the other indices as being of moderate quality.

CONCLUSION

Acupuncture as an adjunct to antidepressants may enhance the therapeutic effectiveness and reduce the adverse drug reactions in patients receiving antidepressants. These findings must be interpreted with caution, as the evidence was of low or moderate quality and there was a lack of comparative data with a placebo control.

SYSTEMATIC REVIEW REGISTRATION

INPLASY202150008.

SparNet: A Convolutional Neural Network for EEG Space-Frequency Feature Learning and Depression Discrimination

Depression affects many people around the world today and is considered a global problem. Electroencephalogram (EEG) measurement is an appropriate way to understand the underlying mechanisms of major depressive disorder (MDD) to distinguish depression from normal control. With the development of deep learning methods, many researchers have adopted deep learning models to improve the classification accuracy of depression recognition. However, there are few studies on designing convolution filters for spatial and frequency domain feature learning in different brain regions. In this study, SparNet, a convolutional neural network composed of five parallel convolutional filters and the SENet, is proposed to learn EEG space-frequency domain characteristics and distinguish between depressive and normal control. The model is trained and tested by the cross-validation method of subject division. The results show that SparNet achieves a sensitivity of 95.07%, a specificity of 93.66%, and an accuracy of 94.37% in classification. Therefore, our results can conclude that the proposed SparNet model is effective in detecting depression using EEG signals. It also indicates that the combination of spatial information and frequency domain information is an effective way to identify patients with depression.

An insight into diagnosis of depression using machine learning techniques: a systematic review

BACKGROUND

In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis.

METHODS

This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis.

RESULTS

A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test.

CONCLUSION

The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.

Alterations in patients with first-episode depression in the eyes-open and eyes-closed conditions: A resting-state EEG study

Altered resting-state EEG activity has been repeatedly reported in major depressive disorder (MDD), but no robust biomarkers have been identified until now. The poor consistency of EEG alterations may be due to inconsistent resting conditions; that is, the eyes-open (EO) and eyes-closed (EC) conditions. Here, we explored the effect of the EO and EC conditions on EEG biomarkers for discriminating MDD subjects and healthy control (HC) subjects. EEG data were recorded from 30 first-episode MDD and 26 HC subjects during an 8-min resting-state session. The features were extracted using spectral power, Lempel-Ziv complexity, and detrended fluctuation analysis. Significant features were further selected via the sequential backward feature selection algorithm. Support vector machine (SVM), logistic regression, and linear discriminate analysis were used to determine a better resting condition to provide more reliable estimates for identifying MDD. Compared with the HC group, we found that the MDD group exhibited widespread increased β and γ powers (p < 0.01) in both conditions. In the EO condition, the MDD group showed increased complexity and scaling exponents in the α band relative to HC subjects (p < 0.05). The best classification performance of the combined feature sets was found in the EO condition, with the leave-one-out classification accuracy of 89.29%, sensitivity of 90.00%, and specificity of 88.46% using SVM with the linear kernel classifier when the threshold was set to 0.7, followed by the β and γ spectral features with an average accuracy of 83.93%. Overall, EO and EC conditions indeed affected the between-group variance, and the EO condition is suggested as the more separable resting condition to identify depression. Specially, the β and γ powers are suggested as potential biomarkers for first-episode MDD.

Mechanism of Depression through Brain Function Imaging of Depression Patients and Normal People

In recent years, functional magnetic resonance technology has discovered that abnormal connections in different brain regions of the brain may serve as the pathophysiological mechanism of mental illness. Exploring the mechanism of information flow and integration between different brain regions is of great significance for understanding the pathophysiological mechanism of mental illness. This article aims to analyze the mechanism of depression by comparing human brain images of normal people and patients with depression and conduct research. Fluoxetine, a selective 5-HT reuptake inhibitor (SSRI) widely used in clinical practice, can selectively inhibit 5-HT transporter and block the reuptake of 5-HT by the presynaptic membrane. The effect of 5-HT is prolonged and increased, thereby producing antidepressant effects. It has low affinity for adrenergic, histaminergic, and cholinergic receptors and has a weaker effect, resulting in fewer adverse reactions. This paper uses the comparative experiment method and the Welch method and uses the average shortest path length L to describe the average value of the shortest path length between two nodes in the network. Attention refers to the ability of a person's mental activity to point and to concentrate on something. Sustained attention means that attention is kept on a certain cognitive object or activity for a certain period of time, which is also called the stability of attention. The research on attention of depression patients generally focuses on continuous attention, and the results obtained show inconsistencies. Most studies have shown that the sustained attention of the depression group is significantly worse than that of the healthy control group. An overview of magnetic resonance imaging technology and an analysis of depression based on resting state were carried out. The key brain areas of the sample core network were scanned, and the ALFF results were analyzed. The data showed that the severity of depression in the depression group was negatively correlated with the ReHo value in the posterior left cerebellum (P=0.010). The sense of despair was negatively correlated with the ReHo value in the posterior right cerebellum (P=0.013). The diurnal variation was negatively correlated with the ReHo value of the left ring (P=0.014). It was positively correlated with the ReHo value of the left ventricle (P=0.048). This experiment has better completed the research on the mechanism of depression by analyzing the functional images of patients with depression and normal human brain.

Machine learning approaches for diagnosing depression using EEG: A review

Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.

MDD-TSVM: A novel semisupervised-based method for major depressive disorder detection using electroencephalogram signals

Major depressive disorder (MDD) is a common mental illness characterized by persistent feeling of depressed mood and loss of interest. It would cause, in a severe case, suicide behaviors. In clinical settings, automatic MDD detection is mainly based on electroencephalogram (EEG) signals with supervised learning techniques. However, supervised-based MDD detection methods encounter two ineviTable bottlenecks: firstly, such methods rely heavily on an EEG training dataset with MDD labels annotated by a physical therapist, leading to subjectivity and high cost; secondly, most of EEG signals are unlabeled in a real scenario. In this paper, a novel semisupervised-based MDD detection method named MDD-TSVM is presented. Specifically, the MDD-TSVM utilizes the semisupervised method of transductive support vector machine (TSVM) as its backbone, further dividing the unlabeled penalty item of the TSVM objective function into two pseudo-labeled penalty items with or without MDD. By such improvement, the MDD-SVM can make full use of labeled and unlabeled datasets as well as alleviate the class imbalance problem. Experiment results showed that our proposed MDD-TSVM achieved F1 score of 0.85 ± 0.05 and accuracy of 0.89 ± 0.03 on identifying MDD patients, which is superior to the state-of-the-art methods.

EEG Analysis with Wavelet Transform under Music Perception Stimulation

In order to improve the classification accuracy and reliability of emotional state assessment and provide support and help for music therapy, this paper proposes an EEG analysis method based on wavelet transform under the stimulation of music perception. Using the data from the multichannel standard emotion database (DEAP), α, ß, and θ rhythms are extracted in frontal (F3 and F4), temporal (T7 and T8), and central (C3 and C4) channels with wavelet transform. EMD is performed on the extracted EEG rhythm to obtain intrinsic mode function (IMF) components, and then, the average energy and amplitude difference eigenvalues of IMF components of EEG rhythm waves are further extracted, that is, each rhythm wave contains three average energy characteristics and two amplitude difference eigenvalues so as to fully extract EEG feature information. Finally, emotional state evaluation is realized based on a support vector machine classifier. The results show that the correct rate between no emotion, positive emotion, and negative emotion can reach more than 90%. Among the pairwise classification problems among the four emotions selected, the classification accuracy obtained by this EEG feature extraction method is higher than that obtained by general feature extraction methods, which can reach about 70%. Changes in EEG α wave power were closely correlated with the polarity and intensity of emotion; α wave power varied significantly between "happiness and fear," "pleasure and fear," and "fear and sadness." It has a good application prospect in both psychological and physiological research of emotional perception and practical application.

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

EEG-based Major Depressive Disorder Detection Using Data Mining Techniques

Major depressive disorder (MDD) is a common mental illness characterized by a persistent feeling of low mood, sadness, fatigue, despair, etc.. In a serious case, patients with MDD may have suicidal thoughts or even suicidal behaviors. In clinical practice, a widely used method of MDD detection is based on a professional rating scale. However, the scale-based diagnostic method is highly subjective, and requires a professional assessment from a trained staff. In this work, 92 participants were recruited to collect EEG signals in the Shenzhen Traditional Chinese Medicine Hospital, assessing MDD severity with the HAMD-17 rating scale by a trained physician. Two data mining methods of logistic regression (LR) and support vector machine (SVM) with derived EEG-based beta-alpha-ratio features, namely LR-DF and SVM-DF, are employed to screen out patients with MDD. Experimental results show that the presented the LR-DF and SVM-DF achieved F 1 scores of 0:76 0:30 and 0:92 0:18, respectively, which have obvious superiority to the LR and SVM without derived EEG-based beta-alpha-ratio features.

Minocycline inhibits sleep deprivation-induced aberrant microglial activation and Keap1-Nrf2 expression in mouse hippocampus

Sleep deprivation (SD) is a hallmark of modern society and associated with many neuropsychiatric disorders, including depression and anxiety. However, the cellular and molecular mechanisms underlying SD-associated depression and anxiety remain elusive. Does the neuroinflammation play a role in mediating the effects of SD? In this study, we investigated SD-induced cellular and molecular alterations in the hippocampus and asked whether treatment with an anti-inflammatory drug, minocycline, could attenuate these alterations. We found that SD animals exhibit activated microglia and decreased levels of Keap1 and Nrf2 (antioxidant and anti-inflammatory factors) in the hippocampus. In vivo local field potential recordings show decreased theta and beta oscillations, but increased high gamma oscillations, as a result of SD. Behavioral analysis revealed increased immobility time in the forced swim and tail suspension tests, and decreased sucrose intake in SD mice, all indicative of depressive-like behavior. Moreover, open field test and elevated plus maze test results indicated that SD increases anxiety-like behavior. Interestingly, treatment with the microglial modulator minocycline prevented SD-induced microglial activation, restored Keap1 and Nrf2 levels, normalized neuronal oscillations, and alleviated depressive-like and anxiety-like behavior. The present study reveals that microglial activation and Keap1-Nrf2 signaling play a crucial role in SD-induced behavioral alteration, and that minocycline treatment has a protective effect on these alterations.

A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis

BACKGROUND

Major depressive disorder (MDD) is a prevalent mental illness that is diagnosed through questionnaire-based approaches; however, these methods may not lead to an accurate diagnosis. In this regard, many studies have focused on using electroencephalogram (EEG) signals and machine learning techniques to diagnose MDD.

NEW METHOD

This paper proposes a machine learning framework for MDD diagnosis, which uses different types of EEG-derived features. The features are extracted using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis methods. The sequential backward feature selection (SBFS) algorithm is also employed to perform feature selection. Various classifier models are utilized to select the best one for the proposed framework.

RESULTS

The proposed method is validated with a public EEG dataset, including the EEG data of 34 MDD patients and 30 healthy subjects. The evaluation of the proposed framework is conducted using 10-fold cross-validation, providing the metrics such as accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR). The best performance of the proposed method has provided an average AC of 99%, SE of 98.4%, SP of 99.6%, F1 of 98.9%, and FDR of 0.4% using the support vector machine with RBF kernel (RBFSVM) classifier.

COMPARISON WITH EXISTING METHODS

The obtained results demonstrate that the proposed method outperforms other approaches for MDD classification based on EEG signals.

CONCLUSIONS

According to the obtained results, a highly accurate MDD diagnosis would be provided using the proposed method, while it can be utilized to develop a computer-aided diagnosis (CAD) tool for clinical purposes.

EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System

Purpose

Anxiety disorder is one of the psychiatric disorders that involves extreme fear or worry, which can change the balance of chemicals in the brain. To the best of our knowledge, the evaluation of anxiety state is still based on some subjective questionnaires and there is no objective standard assessment yet. Unlike other methods, our approach focuses on study the neural changes to identify and classify the anxiety state using electroencephalography (EEG) signals.

Methods

We designed a closed neurofeedback experiment that contains three experimental stages to adjust subjects’ mental state. The EEG resting state signal was recorded from thirty-four subjects in the first and third stages while EEG-based mindfulness recording was recorded in the second stage. At the end of each stage, the subjects were asked to fill a Visual Analogue Scale (VAS). According to their VAS score, the subjects were classified into three groups: non-anxiety, moderate or severe anxiety groups.

Results

After processing the EEG data of each group, support vector machine (SVM) classifiers were able to classify and identify two mental states (non-anxiety and anxiety) using the Power Spectral Density (PSD) as patterns. The highest classification accuracies using Gaussian kernel function and polynomial kernel function are 92.48 ±   1.20% and 88.60   ±   1.32%, respectively. The highest average of the classification accuracies for healthy subjects is 95.31 ±   1.97% and for anxiety subjects is 87.18 ±   3.51%.

Conclusions

The results suggest that our proposed EEG neurofeedback-based classification approach is efficient for developing affective BCI system for detection and evaluation of anxiety disorder states.

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression

To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG's asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG's asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients.

An Improved Classification Model for Depression Detection Using EEG and Eye Tracking Data

At present, depression has become a main health burden in the world. However, there are many problems with the diagnosis of depression, such as low patient cooperation, subjective bias and low accuracy. Therefore, reliable and objective evaluation method is needed to achieve effective depression detection. Electroencephalogram (EEG) and eye movements (EMs) data have been widely used for depression detection due to their advantages of easy recording and non-invasion. This research proposes a content based ensemble method (CBEM) to promote the depression detection accuracy, both static and dynamic CBEM were discussed. In the proposed model, EEG or EMs dataset was divided into subsets by the context of the experiments, and then a majority vote strategy was used to determine the subjects' label. The validation of the method is testified on two datasets which included free viewing eye tracking and resting-state EEG, and these two datasets have 36,34 subjects respectively. For these two datasets, CBEM achieves accuracies of 82.5% and 92.65% respectively. The results show that CBEM outperforms traditional classification methods. Our findings provide an effective solution for promoting the accuracy of depression identification, and provide an effective method for identificationof depression, which in the future could be used for the auxiliary diagnosis of depression.