Diagnosis Of Mental Diseases Research Articles

Characteristics of mental disorders among information technology workers in 238 compensated cases in Japan.

This study aimed to investigate the circumstances, characteristics, and background factors of mental disorders within the information technology (IT) industry, which is known for the frequent occurrence of mental disorders from overwork and poor psychosocial work environment. The study analyzed 238 cases of mental disorders and suicides in the IT industry from fiscal years 2010 to 2018 on the database constructed by the Japan Research Center for Overwork-Related Disorders. The results showed a higher number of cases among men (n=178, 74.8%) and an increasing number among women. The data for diagnosis of mental diseases indicated a high number of depressive episodes (150 cases, 63.0%). Regarding work-induced psychological burden, 37 cases (15.5%) reported "extremely long working hours" for "special events" and 65 cases (27.3%) reported "constantly long working hours". For "specific events", 87 cases (36.6%) reportedly "caused (major) changes in the content and amount of work". Among women, 13 cases (21.7%) of "sexual harassment" (SH), which showed an increasing trend. Therefore, in the IT industry, the working hours must be properly managed. Mental health measures should also accompany improved psychosocial environment in the workplace, and prohibition of SH at work in particular.

Artificial Intelligence for Mental Health: A Double-Edged Sword

Artificial intelligence (AI) in the context of mental health presents a complex scenario characterized by both advantageous prospects and inherent hazards. AI possesses the capacity to fundamentally transform the field of mental healthcare through the provision of individualized and readily accessible assistance. AI systems possess the capacity to analyze extensive quantities of data and identify patterns, thereby offering valuable assistance to doctors in the diagnosis of mental diseases, development of personalized treatment programs, and even prediction of relapses. Furthermore, there is ongoing development in the field of AI to create chatbots that utilize AI technology and serve as virtual therapists. These chatbots aim to provide individuals with continuous emotional support, available at any time of the day. Nonetheless, this technological advancement also gives rise to apprehensions about issues of privacy, ethical considerations, and an excessive dependence on automated systems. Robust security measures are crucial in safeguarding the anonymity of consumers while utilizing AI systems due to the sensitive nature of mental health data. Furthermore, it is important to consider the potential for dehumanization when patients only depend on interventions driven by machines, as opposed to fostering human connection and empathy. Achieving an optimal equilibrium between technical progress and preserving interpersonal connection remains a key aspect in fully using the capabilities of AI in the field of mental health, while simultaneously ensuring its effects on individual welfare are protected.

Non-targeted Metabolomics Analysis Reveals Distinct Metabolic Profiles Between Positive and Negative Emotional Tears of Humans: A Preliminary Study.

Background Basal, reflex, and emotional tears differ in chemical components. It is not yet known whether chemical differences exist in tears of different emotions. We investigated the biochemical basis of emotional tears by performing non-targeted metabolomics analyses of positive and negative emotional tears of humans. Methods Samples of reflex, negative, and positive emotional tears were obtained from 12 healthy college participants (11 females and one male). Untargeted metabolomics was performed to identify metabolites in different types of tears. The differentially altered metabolites were screened and assessed using univariate and multivariate analyses. Results The orthogonal partial least squares discriminant analysis model showed that reflex, negative, and positive emotional tears were clearly separated. A total of 133 significantly differentially expressed metabolites of electrospray ionization source (ESI-) mode were identified between negative and positive emotional tears. The top 50 differentially expressed metabolites between negative and positive emotional tears were highly correlated. Pathway analysis revealed that secretion of negative emotional tears was associated with some synapses in the brain, regulation of a series of endocrine hormones, including the estrogen signaling pathway, and inflammation activities, while secretion of positive emotional tears was correlated with biotin and caffeine metabolism. Conclusions It is indicated that metabolic profiles of reflex, positive, and negative emotional tears of humans are distinct, and secretion of the tears involves distinct biological activities. Therefore, we present a chemical method for detecting human emotions, which may become a powerful tool for the diagnosis of mental diseases and the identification of fake tears.

Current development of biosensing technologies towards diagnosis of mental diseases.

The biosensor is an instrument that converts the concentration of biomarkers into electrical signals for detection. Biosensing technology is non-invasive, lightweight, automated, and biocompatible in nature. These features have significantly advanced medical diagnosis, particularly in the diagnosis of mental disorder in recent years. The traditional method of diagnosing mental disorders is time-intensive, expensive, and subject to individual interpretation. It involves a combination of the clinical experience by the psychiatrist and the physical symptoms and self-reported scales provided by the patient. Biosensors on the other hand can objectively and continually detect disease states by monitoring abnormal data in biomarkers. Hence, this paper reviews the application of biosensors in the detection of mental diseases, and the diagnostic methods are divided into five sub-themes of biosensors based on vision, EEG signal, EOG signal, and multi-signal. A prospective application in clinical diagnosis is also discussed.

Multimodal emotion recognition using cross modal audio-video fusion with attention and deep metric learning

In the last few years, the multi-modal emotion recognition has become an important research issue in the affective computing community due to its wide range of applications that include mental disease diagnosis, human behavior understanding, human machine/robot interaction or autonomous driving systems. In this paper, we introduce a novel end-to-end multimodal emotion recognition methodology, based on audio and visual fusion designed to leverage the mutually complementary nature of features while maintaining the modality-specific information. The proposed method integrates spatial, channel and temporal attention mechanisms into a visual 3D convolutional neural network (3D-CNN) and temporal attention into an audio 2D convolutional neural network (2D-CNN) to capture the intra-modal features characteristics. Further, the inter-modal information is captured with the help of an audio-video (A-V) cross-attention fusion technique that effectively identifies salient relationships across the two modalities. Finally, by considering the semantic relations between the emotion categories, we design a novel classification loss based on an emotional metric constraint that guides the attention generation mechanisms. We demonstrate that by exploiting the relations between the emotion categories our method yields more discriminative embeddings, with more compact intra-class representations and increased inter-class separability. The experimental evaluation carried out on the RAVDESS (The Ryerson Audio-Visual Database of Emotional Speech and Song), and CREMA-D (Crowd-sourced Emotional Multimodal Actors Dataset) datasets validates the proposed methodology, which leads to average accuracy scores of 89.25% and 84.57%, respectively. In addition, when compared to state-of-the-art techniques, the proposed solution shows superior performances, with gains in accuracy ranging in the [1.72%, 11.25%] interval.

Deep learning based depression detection from social media text

Depression perception be a complex task on social media Complex properties associated with mental illness. There was a recent development in this area of research; social media platforms have established themselves as growing popularity. A basic part of people`s daily life. Social media platforms and their users share goals Relationships where the user's personal life is reflected on these platforms at several levels. Aside from the complexity associated with detecting mental illness through social media platforms, it is inherently difficult to obtain an enough annotated training data, so a supervised deep learning approach such as deep neural networks the implementation has not yet been widely implemented. We tried to find them for these reasons. The most effective deep learning model of the architectures selected in the previous architecture Achievements of supervised learning methods. The selected model will be used for recognition online users showing depression. Due to the limited amount of unstructured text data Extracted from social media text. Recently, Deep learning has been effectively used to a variety of application challenges, including stock market forecasting, traffic flow and accident risk forecasting, and mental disease diagnosis. Furthermore, deep learning has been used to predict sadness on social media and has outperformed classical machine learning method.

Expert system on diagnosis of mental diseases

Mental disorder is one of the most serious problems in today's time. Mental disorders can be classified into different sub-disorders according to changes in human behavior and mental condition. According to reports one out of seven people suffered from mental disorders. In this research paper, our main emphasis is to build an expert system that diagnoses people based on their symptoms, so people can diagnose themselves early before going to the doctor. Expert Systems are one of the most important applications in artificial intelligence that solves complex problems without human help. We provide different rules, facts, and relationships among different symptoms in our knowledge base, from which users can query their problems and get their results. We used SWI-prolog to build an expert system. There are a few types of disorders, such as mental disorders, neurodevelopmental disorders, eating disorders, etc.

Multimodal Machine Learning for Mental Disorder Detection:A Scoping Review

Recent advancements in machine learning and multimedia technologies have paved new ways for automatic medical diagnosis. In mental health, multimodal inputs such as visual and audible sensing data are promising to investigate the underlying mechanisms of many conditions, such as depression and bipolar disorders. With the increasing burden on healthcare systems, timely diagnosis of mental diseases using multiple modalities might benefit millions of people worldwide. This scoping review provides an exploratory overview of recent multimodal machine learning approaches for mental disorder screening. We also discuss a generalised end-to-end multimodal machine learning pipeline for future research and development of multimodal disease detection.

An automated deep learning enabled brain signal classification for epileptic seizure detection on complex measurement systems

Recent advancements in machine learning and deep learning models find them helpful in designing effective complex measurement systems. At the same time, examining the brain’s activities using Electroencephalography (EEG) is essential to determine the mental state or thought of a person. It is essential in several application areas, such as Brain-Computer Interface (BCI), emotion recognition, and mental disease diagnosis. The proper brain signal classification using EEG finds helpful diagnose epileptic seizures. Since the traditional seizure detection process is a lengthy and challenging task, the automated identification of epilepsy is a significant problem. In order to resolve the issues that exist in the traditional brain signal classification models, this study designs Automated Deep Learning-Enabled Brain Signal Classification for Epileptic Seizure Detection (ADLBSC-ESD). The proposed ADLBSC-ESD technique aims to classify the brain signals to determine the existence of seizures or not. In addition, the presented model involves the design of the Improved Teaching and Learning-Enabled Optimization (ITLBO) technique for selecting features from EEG signals. Moreover, the Deep Belief Network (DBN) model is used for an effectual classification of EEG signals, and the hyperparameters of the DBN model are optimally tuned using the Swallow Swarm Optimization Algorithm (SSA). In order to ensure the improved brain signal classification performance of the ADLBSC-ESD technique, a series of simulations take place, and the outcomes are investigated concerning different measures. The experimental values highlighted the better performance of the ADLBSC-ESD technique over the current state of art techniques with maximum accuracy of 0.8316 and 0.8609 under binary and multiple classes, respectively.

Multi-Pose Facial Expression Recognition Using Hybrid Deep Learning Model with Improved Variant of Gravitational Search Algorithm

The recognition of human facial expressions with the variation of poses is one of the challenging tasks in real-time applications such as human physiological interaction detection, intention analysis, marketing interest evaluation, mental disease diagnosis, etc. This research work addresses the problem of expression recognition from different facial poses at the yaw angle. The major contribution of the paper is the proposal of an autonomous pose variant facial expression recognition framework using the amalgamation of a hybrid deep learning model with an improved quantum inspired gravitational search algorithm. The hybrid deep learning model is the integration of the convolutional neural network and recurrent neural network. The applicability of the hybrid deep learning model can be considered as significant if the feature set is efficiently optimized to have the discriminative features respective to each expression class. Here, the Improved Quantum Inspired Gravitational Search Algorithm (IQI-GSA) is utilized for the selection and optimization of features. The IQI-GSA method is significant for optimizing the features compared to quantum-behaved binary gravitation search algorithm for handing the local optima and stochastic characteristics. Comparing with state-of-art techniques, the proposed framework exhibits the outperformed recognition rate for experimentation on Karolinska Directed Emotional Faces (KDEF) and Japanese Female Facial Expression (JAFFE) datasets.

Brain Entropy Study on Obsessive-Compulsive Disorder Using Resting-State fMRI.

Object: Brain entropy is a potential index in the diagnosis of mental diseases, but there are some differences in different brain entropy calculation, which may bring confusion and difficulties to the application of brain entropy. Based on the resting-state function magnetic resonance imaging (fMRI) we analyzed the differences of the three main brain entropy in the statistical significance, including approximate entropy (ApEn), sample entropy (SampEn) and fuzzy entropy (FuzzyEn), and studied the physiological reasons behind the difference through comparing their performance on obsessive-compulsive disorder (OCD) and the healthy control (HC).Method: We set patients with OCD as the experimental group and healthy subjects as the control group. The brain entropy of the OCD group and the HC are calculated, respectively, by voxel and AAL region. And then we analyzed the statistical differences of the three brain entropies between the patients and the control group. To compare the sensitivity and robustness of these three kinds of entropy, we also studied their performance by using certain signal mixed with noise.Result: Compare with the control group, almost the whole brain's ApEn and FuzzyEn of OCD are larger significantly. Besides, there are more brain regions with obvious differences when using ApEn comparing to using FuzzyEn. There was no statistical difference between the SampEn of OCD and HC.Conclusion: Brain entropy is a numerical index related to brain function and can be used as a supplementary biological index to evaluate brain state, which may be used as a reference for the diagnosis of mental illness. According to an analysis of certain signal mixed with noise, we conclude that FuzzyEn is more accurate considering sensitivity, stability and robustness of entropy.

Association between noise annoyance and mental health outcomes - an evidence review

To date, most reviews on noise and mental health have focused on noise exposure. However, a number of studies found associations between mental health and noise annoyance, but not with exposure. A literature search was carried out in PubMed and Web of Science databases. We also hand-searched conference proceedings and references in other systematic reviews on noise exposure and annoyance/mental health. We identified 25 articles through the databases searches that satisfied the inclusion criteria; existing literature reviews provided two additional publications. The majority of identified studies used either a 5-point (n=15) or 11-point scale (n=5) to measure noise annoyance. The sources of noise annoyance mainly come from traffic (n=18 or 67%), and neighbourhood (n=4 or 15%). For mental health outcomes, 20 (74%), 2 (7%) and 2 (7%) articles used validated questionnaires, self-reported use of anxiolytics/antidepressants, or self-reported diagnosis of mental disease, respectively, to assess mental health. Six articles differentiated between depression and anxiety disorder while 19 focused on general mental health. Results from these studies overall point to an adverse association of noise annoyance (high noise annoyance in particular) with depression, anxiety or general mental problem, either measured by self-reported diagnosis, self-reported medicine use or questionnaires.

Utterance Level Feature Aggregation with Deep Metric Learning for Speech Emotion Recognition.

Emotion is a form of high-level paralinguistic information that is intrinsically conveyed by human speech. Automatic speech emotion recognition is an essential challenge for various applications; including mental disease diagnosis; audio surveillance; human behavior understanding; e-learning and human–machine/robot interaction. In this paper, we introduce a novel speech emotion recognition method, based on the Squeeze and Excitation ResNet (SE-ResNet) model and fed with spectrogram inputs. In order to overcome the limitations of the state-of-the-art techniques, which fail in providing a robust feature representation at the utterance level, the CNN architecture is extended with a trainable discriminative GhostVLAD clustering layer that aggregates the audio features into compact, single-utterance vector representation. In addition, an end-to-end neural embedding approach is introduced, based on an emotionally constrained triplet loss function. The loss function integrates the relations between the various emotional patterns and thus improves the latent space data representation. The proposed methodology achieves 83.35% and 64.92% global accuracy rates on the RAVDESS and CREMA-D publicly available datasets, respectively. When compared with the results provided by human observers, the gains in global accuracy scores are superior to 24%. Finally, the objective comparative evaluation with state-of-the-art techniques demonstrates accuracy gains of more than 3%.

NEUROMARKERS OF ANXIETY IN A PATIENT WHO HAD EXPERIENCED A TRANSIENT ISCHEMIC ATTACK (TIA) OF THE LEFT BRAIN HEMISPHERE AS A RESULT OF A CRITICAL STENOSIS OF THE INTERNAL CAROTID ARTERY (ICA) OPERATED ON BY ENDARTERECTOMY (CEA)

The search for neuromarkers is a very promising way to improve psychiatric and psychological care. They are now considered to be an innovative diagnostic tool in psychiatry and neuropsychology, but more broadly in all human health sciences. The aim of our study was to find the neuromarker of anxiety in a patient who had experienced a Transient IschemicAttack (TIA) of the left brain hemisphere as a result of a critical stenosis of the Internal Carotid Artery (ICA) operated on byendarterectomy (CEA). We will present the case of a 54-year-old man,an architect, who experienced a Transient Ischemic Attack (TIA) of the left brain hemispherecaused by a critical stenosis of theInternal Carotid Artery (ICA) and was treated successfully with surgical endarterectomy (CEA). One year after the surgery itself, the patient developed severe postoperative anxiety, headaches, difficulty in sleepingas well as the inability to continue working in his profession. Strong anxiety was notedon the adapted 100-millimeter Visual Analogue Anxiety Scale (VAAS). The patient was assessed using the Human Brain Index (HBI) methodology (Kropotov 2009; 2016; 2017; Pąchalska, Kaczmarek&Kropotov 2014) which consisted of recording 19-channel EEG in resting state conditions, during the cued GO/NOGO task and comparing the parameters of EEG spectra and Event-Related Potentials (ERPs) with the normative and patient databases of the Human Brain Index(HBI). No signs of cognitive dysfunction was found, however an excessive Rolandic beta was observed. In line with the working hypothesis as to the presence of an anxiety neuromarker, the patient’s studies confirmed an increased P1 time wave in the left hemisphere of the brain in ERP in response to visual stimuli, i.e. an anxiety neuromarker. Following the detection of this neuromarkera specific anodic Transcranial Direct Current Stimulations (tDCS) protocol was proposed (see: Kropotov 2016; Pąchalska, Kaczmarek & Kropotov 2020). Ten tDCS sessions were performed and the postoperativeanxiety was found to be resolved. The patient returned to work. The use of Human Brain Index (HBI) methodologyenabling the isolation of the Event Related Potentials (ERPs) patterns revealed the presence of a distinct anxietyneuromarker. Neurotherapy with the use of tDCS allowed the reduction of anxiety symptoms and the patient’s return to work. The above case study indicates the necessity to use new neurotechnologies in the diagnosis of mental diseases, with particular emphasis on postoperative anxiety.

Image Classification in HTP Test Based on Convolutional Neural Network Model

HTP test in psychometrics is a widely studied and applied psychological assessment technique. HTP test is a kind of projection test, which refers to the free expression of painting itself and its creativity. Therefore, the form of group psychological counselling is widely used in mental health education. Compared with traditional neural networks, deep learning networks have deeper and more network layers and can learn more complex processing functions. In this stage, image recognition technology can be used as an assistant of human vision. People can quickly get the information in the picture through retrieval. For example, you can take a picture of an object that is difficult to describe and quickly search the content related to it. Convolutional neural network, which is widely used in the image classification task of computer vision, can automatically complete feature learning on the data without manual feature extraction. Compared with the traditional test, the test can reflect the painting characteristics of different groups. After quantitative scoring, it has good reliability and validity. It has high application value in psychological evaluation, especially in the diagnosis of mental diseases. This paper focuses on the subjectivity of HTP evaluation. Convolutional neural network is a mature technology in deep learning. The traditional HTP assessment process relies on the experience of researchers to extract painting features and classification.

A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images.

Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular research field for the computer science and medical engineering. Traditional machine learning and deep learning methods were employed in the classification of brain MRI images in the previous studies. However, the current algorithms rarely take into consideration the influence of multi-scale brain connectivity disorders on some mental diseases. To improve this defect, a deep learning structure was proposed based on MRI images, which was designed to consider the brain's connections at different sizes and the attention of connections. In this work, a Multiscale View (MV) module was proposed, which was designed to detect multi-scale brain network disorders. On the basis of the MV module, the path attention module was also proposed to simulate the attention selection of the parallel paths in the MV module. Based on the two modules, we proposed a 3D Multiscale View Convolutional Neural Network with Attention (3D MVA-CNN) for classification of MRI images for mental disease. The proposed method outperformed the previous 3D CNN structures in the structural MRI data of ADHD-200 and the functional MRI data of schizophrenia. Finally, we also proposed a preliminary framework for clinical application using 3D CNN, and discussed its limitations on data accessing and reliability. This work promoted the assisted diagnosis of mental diseases based on deep learning and provided a novel 3D CNN method based on MRI data.

Online grooming: an analysis of the phenomenon.

Since its diffusion in the 1990s, the Internet has developed into a fully integrated component of the lives of teenagers across the globe. As use of the Internet by underage youth has evolved and increased over the past decades, concerns about how technology may contribute to minors becoming victims of online sex crimes, including online grooming, have heightened. The present literature review contributes to the existing knowledge base regarding the epidemiology of the online grooming process, exploring key themes and issues arising in this area. We conducted a review of the current literature by an initial database research of papers published since 1990. Three independent reviewers selected relevant articles, initially based on title and abstract analysis, then by full text in order to make a final determination. After the final selection, a total of 37 articles were reviewed. The articles reviewed report highly heterogenous results with regards to epidemiological data, estimating a prevalence of the online grooming phenomenon between 9% and 19%. Factors influencing minors' risk of online grooming included increasing age, gender, sexual minority orientation, diagnosis of mental disease, conflictual relationships with parents and risk-taking behaviors (e.g. chat room and social networking site use). Although the true prevalence of online grooming is not available, it is a significant issue among minors. Thus, it is important to educate youth on responsible internet use, starting at the earliest age and continuing during adolescence.

A REVIEW ON FACIAL EXPRESSION RECOGNITION SYSTEMS

Facial expression is one of the most important form of non-verbal communication. Facial expressions emit the feelings of a person, and it allows judging that person by others. Some can understand facial expressions of underlying emotions to some extent, whereas many of us cannot. Facial Expression recognition (FER) system is a system to recognize expressions from a person’s face. It plays an important part in today’s world in fields of mental disease diagnosis, and human social/physiological interaction detection. Various methods of FER exist. This paper provides a summary of various processes involved in FER.

Histogram of Gradient Orientations of Signal Plots Applied to P300 Detection.

The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, subjectively identifying troughs and peaks in Event-Related Potentials (ERP), or by studying graphoelements in pathological sleep stages. Additionally, the discipline of Brain Computer Interfaces (BCI) requires new methods to decode patterns from non-invasive EEG signals. This field is developing alternative communication pathways to transmit volitional information from the Central Nervous System. The technology could potentially enhance the quality of life of patients affected by neurodegenerative disorders and other mental illness. This work mimics what electroencephalographers have been doing clinically, visually inspecting, and categorizing phenomena within the EEG by the extraction of features from images of signal plots. These features are constructed based on the calculation of histograms of oriented gradients from pixels around the signal plot. It aims to provide a new objective framework to analyze, characterize and classify EEG signal waveforms. The feasibility of the method is outlined by detecting the P300, an ERP elicited by the oddball paradigm of rare events, and implementing an offline P300-based BCI Speller. The validity of the proposal is shown by offline processing a public dataset of Amyotrophic Lateral Sclerosis (ALS) patients and an own dataset of healthy subjects.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics.

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.