Gotowa bibliografia na temat „Automated depression estimation”

Major depressive disorder stands as a highly prevalent mental disorder on a global scale. Detecting depression at its early stages holds paramount importance for effective treatment. However, due to the coexistence of depression with other conditions and the subjective nature of diagnosis, early identification poses a significant challenge. In recent times, machine learning techniques have emerged as valuable tools for the development of automated depression estimation systems, aiding in the diagnostic process. In this particular study, a deep learning approach utilizing pupil diameter was employed to distinguish between individuals diagnosed with depression and those who are considered mentally healthy. Pupillometric recordings were collected from a total of 58 individuals, comprising 29 healthy individuals and 29 individuals diagnosed with depression. Pupil size was recorded every 4 ms. The performance of three pretrained convolutional neural networks (GoogLeNet, SqueezeNet, and AlexNet) was evaluated for depression classification using the pupil size data. The highest accuracy of 98.28% was obtained with AlexNet. This finding highlights the potential of utilizing pupil diameter as a reliable indicator for objectively measuring depression.

ObjectivesStructural neuroimaging studies have revealed a consistent pattern of volumetric reductions in both the hippocampus and the anterior cingulate cortex (ACC) of individuals with a major depressive episode (MDE). This study investigated hippocampal and ACC volume differences in the elderly comparing currently depressed individuals and individuals with a past lifetime history of MDE versus healthy controls.MethodsWe studied non-demented individuals from a cohort of community-dwelling people aged 65 and over (ESPRIT study). T1-weighted magnetic resonance images were used to acquire anatomical scans from 150 currently depressed individuals, 79 individuals with at least one past MDE, and 310 healthy controls. We derived quantitative regional estimates of subcortical volume of hippocampus and ACC using FreeSurfer Software (automated method). Concerning hippocampus, we also used a manual method of measurement. General Linear Model was used to study brain volumes in current and past depression adjusting for gender, age, education level, total brain volume, and anxiety disorder comorbidity.ResultsAfter adjustment, current depression was associated with a lower left posterior hippocampal volume (F = 10.38, P = 0.001) using manual estimation of volume. No other significant differences were observed. A positive correlation was found between time since the last MDE and left posterior hippocampal volume.ConclusionsThe finding of left posterior hippocampal volume reduction in currently depressed individuals but not in those with a past MDE compared to healthy controls could be related to brain neuroplasticity. Additionally, our results suggest manual measures to be more sensitive than automated methods.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Electrocardiography deals with the electrical activity of the heart. The condition of cardiac health is given by the electrocardiogram (ECG). ECG analysis is one of the most important aspects of research in the field of biomedical sciences and healthcare. The precision in the identification of various parameters in the ECG is of great importance for the reliability of an automated ECG analyzing system and diagnosis of cardiac diseases. Many algorithms have been developed in the last few years, each with their own advantages and limitations. In this work, we have developed an algorithm for 12-lead ECG parameter detection which works in three steps. Initially, the signal is denoised by the wavelet transform approach using a graphical programming language called LabVIEW (Laboratory Virtual Instrument Engineering Workbench). Next, primary features are detected from the denoised ECG signal using Matlab, and lastly, the secondary features related to diabetes are estimated from the detected primary features. Diabetes mellitus (DM), which is characterized by raised blood glucose levels in an individual, affects an estimated 2–4% of the world's population, making it one of the major chronic illnesses prevailing today. Recently, there has been increasing interest in the study of relationship between diabetes and cardiac health. Thus, in this work, we estimate diabetic-related secondary ECG features like corrected QT interval (QTc), QT dispersion (QTd), P wave dispersion (PD), and ST depression (STd). Our software performance is evaluated using CSE DS-3 multi-lead data base and the data acquired at SGGS IE & T, Nanded, MS, which contains 5000 samples recorded at a sampling frequency of 500 HZ. The proposed algorithm gives a sensitivity of 99.75% and a specificity of 99.83%.

In view of the tremendous emissions of toxic gases and particulate matter (PM) by low-power firewood-fueled fireplaces, there is an urgent need for effective measures to lower emissions to keep this renewable and economical source for private home heating available in the future. For this purpose, an advanced combustion air control system was developed and tested on a commercial fireplace (HKD7, Bunner GmbH, Eggenfelden, Germany), complemented with a commercial oxidation catalyst (EmTechEngineering GmbH, Leipzig, Germany) placed in the post-combustion zone. Combustion air stream control of the wood-log charge combustion was realized by five different control algorithms to describe all situations of combustion properly. These control algorithms are based on the signals of commercial sensors representing catalyst temperature (thermocouple), residual oxygen concentration (LSU 4.9, Bosch GmbH, Gerlingen, Germany) and CO/HC-content in the exhaust (LH-sensor, Lamtec Mess- und Regeltechnik für Feuerungen GmbH & Co. KG, Walldorf (Germany)). The actual flows of the combustion air streams, as calculated for the primary and secondary combustion zone, are adjusted by motor-driven shutters and commercial air mass flow sensors (HFM7, Bosch GmbH, Gerlingen, Germany) in separate feedback control loops. For the first time, the residual CO/HC-content (CO, methane, formaldehyde, etc.) in the flue gas is in-situ monitored with a long-term stable AuPt/YSZ/Pt mixed potential high-temperature gas sensor, which allows continuous estimation of the flue gas quality with an accuracy of about ±10%. This parameter is not only an essential input for advanced combustion air stream control but also provides monitoring of the actual combustion quality and logging of this value over a whole heating period. By many firing experiments in the laboratory and by field tests over four months, it could be demonstrated that with this long-term stable and advanced automated firing system, depression of the gaseous emissions by about 90% related to manually operated fireplaces without catalyst could be achieved. In addition, preliminary investigations at a firing appliance complemented by an electrostatic precipitator yielded PM emission depression between 70% and 90%, depending on the firewood load.

The paper presents a descriptive model, applicable in agricultural theory and practice, for determining the quantity of alfalfa hay obtained from a land surface, using remote investigations, by geospatial methods and means. The working algorithm was tested in a rural area located in the northern part of Romania, in the Humor Depression, and the data acquisition was made with DJI Phantom 4 Pro - Unmanned Aerial Vehicle equipment. For the automated calculation of the amount of alfalfa hay harvested from a certain surface and stored as haystacks, the following steps were carried out: processing the images acquired with the drone to obtain the point clouds, determining the 3D model of the haystacks, calculating the volume of hay stored in the stacks and converting the volume in quantity of hay/surface. As a result of the measurements and calculations carried out, a quantity of hay of 11.96 tons/ha was obtained, data verified and validated by the researches from the specialized literature. Compared with the agronomic methods, the use of the geomatics techniques, to determine the quantity of hay harvested from an agricultural area, presents a series of practical and economic advantages: they exclude the manual measurements in the field and, therefore, the displacements on extended surfaces; reduce the working time; have high precision because, for the estimation of the haystacks volume, three-dimensional models are used, instead of the traditional mathematical formulas. At the same time, geospatial data is acquired through drone flying, which can be used in other types of analysis. The working algorithm can also be applied to other studied objectives or research topics.

Depression is a severe psychological condition that affects millions of people worldwide. As depression has received more attention in recent years, it has become imperative to develop automatic methods for detecting depression. Although numerous machine learning methods have been proposed for estimating the levels of depression via audio, visual, and audiovisual emotion sensing, several challenges still exist. For example, it is difficult to extract long-term temporal context information from long sequences of audio and visual data, and it is also difficult to select and fuse useful multi-modal information or features effectively. In addition, how to include other information or tasks to enhance the estimation accuracy is also one of the challenges. In this study, we propose a multi-modal adaptive fusion transformer network for estimating the levels of depression. Transformer-based models have achieved state-of-the-art performance in language understanding and sequence modeling. Thus, the proposed transformer-based network is utilized to extract long-term temporal context information from uni-modal audio and visual data in our work. This is the first transformer-based approach for depression detection. We also propose an adaptive fusion method for adaptively fusing useful multi-modal features. Furthermore, inspired by current multi-task learning work, we also incorporate an auxiliary task (depression classification) to enhance the main task of depression level regression (estimation). The effectiveness of the proposed method has been validated on a public dataset (AVEC 2019 Detecting Depression with AI Sub-challenge) in terms of the PHQ-8 scores. Experimental results indicate that the proposed method achieves better performance compared with currently state-of-the-art methods. Our proposed method achieves a concordance correlation coefficient (CCC) of 0.733 on AVEC 2019 which is 6.2% higher than the accuracy (CCC = 0.696) of the state-of-the-art method.

Abstract: Stress and Depression is one of the most widely recognized and handicapping mental issue that relevantly affects society. Automatic health monitoring systems could be crucial and important to improve depression and stress detection system using social networking. Sentiment Analysis alludes to the utilization of natural language processing and content mining approaches planning to recognize feeling or opinion. Full of feeling Computing is the examination and advancement of frameworks and gadgets that can perceive, decipher, process, and mimic human effects. Sentiment Analysis and deep learning techniques could give powerful algorithms and frameworks to a target appraisal and observing of mental issue and, specifically of depression and stress. In this paper, the application of sentiment analysis and deep learning methodologies to depression and stress detection and monitoring are discussed. In addition, a fundamental plan of an incorporated multimodal framework for stress and depression checking, that incorporates estimation investigation and full of feeling processing strategies, is proposed. In particular, the paper traces the fundamental issues and moves comparative with the structure of such a framework.

Background: Electroencephalography (EEG) may be used as an objective diagnosis tool for diagnosing various disorders. Recently, source localization from EEG is being used in the analysis of real-time brain monitoring applications. However, inverse problem reduces the accuracy in EEG signal processing systems. Objectives: This paper presents a new method of EEG source localization using variational mode decomposition (VMD) and standardized the low resolution brain electromagnetic tomography (sLORETA) inverse model. The focus is to compare the effectiveness of the proposed approach for EEG signals of depression patients. Method: As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying VMD. Then, closely related functions are analyzed using the inverse modelling-based source localization procedures such as sLORETA. Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. Results: The performance of the algorithm has been assessed using localization error (LE), mean square error and signal to noise ratio output corresponding to simulated EEG dipole sources and real EEG signals for depression. In order to study the spatial resolution for cortical potential distribution, the main focus has been on studying the effects of noise sources and estimating LE of inverse solutions. More accurate and robust localization results show that this methodology is very promising for EEG source localization of depression signals. Conclusion: It can be said that proposed algorithm efficiently suppresses the influence of noise in the EEG inverse problem using simulated EEG activity and EEG database for depression. Such a system may offer an effective solution for clinicians as a crucial stage of EEG pre-processing in automated depression detection systems and may prevent delay in diagnosis.

Elicited upon violation of regularity in stimulus presentation, mismatch negativity (MMN) reflects the brain's ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection whereas P300 is associated with cognitive processes such as updating of the working memory. To date, there has been extensive research on the roles of MMN and P300 individually, because of their potential to be used as clinical markers of consciousness and attention, respectively. Here, we intend to explore with an unsupervised and rigorous source estimation approach, the underlying cortical generators of MMN and P300, in the context of prediction error propagation along the hierarchies of brain information processing in healthy human participants. The existing methods of characterizing the two ERPs involve only approximate estimations of their amplitudes and latencies based on specific sensors of interest. Our objective is twofold: first, we introduce a novel data-driven unsupervised approach to compute latencies and amplitude of ERP components accurately on an individual-subject basis and reconfirm earlier findings. Second, we demonstrate that in multisensory environments, MMN generators seem to reflect a significant overlap of “modality-specific” and “modality-independent” information processing while P300 generators mark a shift toward completely “modality-independent” processing. Advancing earlier understanding that multisensory contexts speed up early sensory processing, our study reveals that temporal facilitation extends to even the later components of prediction error processing, using EEG experiments. Such knowledge can be of value to clinical research for characterizing the key developmental stages of lifespan aging, schizophrenia, and depression.

Compte tenu de l'impact sévère et généralisé de la dépression, des initiatives de recherche significatives ont été entreprises pour définir des systèmes d'évaluation automatisée de la dépression. La recherche présentée dans cette thèse tourne autour des questions suivantes qui restent relativement inexplorées malgré leur pertinence dans le domaine de l'évaluation automatisée de la dépression : (1) le rôle de la structure du discours dans l'analyse de la santé mentale, (2) la pertinence de la représentation de l'entrée pour les capacités prédictives des modèles de réseaux neuronaux, et (3) l'importance de l'expertise du domaine dans la détection automatisée de la dépression.La nature dyadique des entretiens patient-thérapeute garantit la présence d'une structure sous-jacente complexe dans le discours. Dans cette thèse, nous établissons d'abord l'importance des questions du thérapeute dans l'entrée du modèle de réseau neuronal, avant de montrer qu'une combinaison séquentielle des entrées du patient et du thérapeute est une stratégie sous-optimale. Par conséquent, des architectures à vues multiples sont proposées comme moyen d'incorporer la structure du discours dans le processus d'apprentissage des réseaux neuronaux. Les résultats expérimentaux obtenus avec deux encodages de texte différents montrent les avantages des architectures multi-vues proposées, validant la pertinence de la conservation de la structure du discours dans le processus d'apprentissage du modèle.Ayant établi la nécessité de conserver la structure du discours dans le processus d'apprentissage, nous explorons plus avant les représentations textuelles basées sur les graphes. Les recherches menées dans ce contexte mettent en évidence l'impact des représentations d'entrée non seulement pour définir les capacités d'apprentissage du modèle, mais aussi pour comprendre leur processus prédictif. Les graphiques de similitude de phrases et les graphiques de corrélation de mots-clés sont utilisés pour illustrer la capacité des représentations graphiques à fournir des perspectives variées sur la même entrée, en mettant en évidence des informations qui peuvent non seulement améliorer les performances prédictives des modèles, mais aussi être pertinentes pour les professionnels de la santé. Le concept de vues multiples est également incorporé dans les deux structures graphiques afin de mettre en évidence les différences de perspectives entre le patient et le thérapeute au cours d'un même entretien. En outre, il est démontré que la visualisation des structures graphiques proposées peut fournir des informations précieuses indiquant des changements subtils dans le comportement du patient et du thérapeute, faisant allusion à l'état mental du patient.Enfin, nous soulignons le manque d'implication des professionnels de la santé dans le contexte de la détection automatique de la dépression basée sur des entretiens cliniques. Dans le cadre de cette thèse, des annotations cliniques de l'ensemble de données DAIC-WOZ ont été réalisées afin de fournir une ressource pour mener des recherches interdisciplinaires dans ce domaine. Des expériences sont définies pour étudier l'intégration des annotations cliniques dans les modèles de réseaux neuronaux appliqués à la tâche de prédiction au niveau des symptômes dans le domaine de la détection automatique de la dépression. En outre, les modèles proposés sont analysés dans le contexte des annotations cliniques afin d'établir une analogie entre leur processus prédictif et leurs tendances psychologiques et ceux des professionnels de la santé, ce qui constitue une étape vers l'établissement de ces modèles en tant qu'outils cliniques fiables
Given the severe and widespread impact of depression, significant research initiatives have been undertaken to define systems for automated depression assessment. The research presented in this dissertation revolves around the following questions that remain relatively unexplored despite their relevance within automated depression assessment domain; (1) the role of discourse structure in mental health analysis, (2) the relevance of input representation towards the predictive abilities of neural network models, and (3) the importance of domain expertise in automated depression detection.The dyadic nature of patient-therapist interviews ensures the presence of a complex underlying structure within the discourse. Within this thesis, we first establish the importance of therapist questions within the neural network model's input, before showing that a sequential combination of patient and therapist input is a sub-optimal strategy. Consequently, Multi-view architectures are proposed as a means of incorporating the discourse structure within the learning process of neural networks. Experimental results with two different text encodings show the advantages of the proposed multi-view architectures, validating the relevance of retaining discourse structure within the model's training process.Having established the need to retain the discourse structure within the learning process, we further explore graph based text representations. The research conducted in this context highlights the impact of input representations not only in defining the learning abilities of the model, but also in understanding their predictive process. Sentence Similarity Graphs and Keyword Correlation Graphs are used to exemplify the ability of graphical representations to provide varying perspectives of the same input, highlighting information that can not only improve the predictive performance of the models but can also be relevant for medical professionals. Multi-view concept is also incorporated within the two graph structures to further highlight the difference in the perspectives of the patient and the therapist within the same interview. Furthermore, it is shown that visualization of the proposed graph structures can provide valuable insights indicative of subtle changes in patient and therapist's behavior, hinting towards the mental state of the patient.Finally, we highlight the lack of involvement of medical professionals within the context of automated depression detection based on clinical interviews. As part of this thesis, clinical annotations of the DAIC-WOZ dataset were performed to provide a resource for conducting interdisciplinary research in this field. Experiments are defined to study the integration of the clinical annotations within the neural network models applied to symptom-level prediction task within the automated depression detection domain. Furthermore, the proposed models are analyzed in the context of the clinical annotations to analogize their predictive process and psychological tendencies with those of medical professionals, a step towards establishing them as reliable clinical tools

An intense seismic activity started on February, 2023, close to Targu Jiu city, in Gorj region (Romania), as part of the Getic Depression. The earthquakes were located nearby the contact between Getic Depression and the Carpathians orogen. The seismic sequence consisted of more than 3100 foreshocks with the highest magnitude of Mw=4.9 (occurred on 13 February 2023), respectively Mw= 5.4 (on 14 February). A stable and automatic method, implemented and optimized in the real time data acquisition and processing system (ANTELOPE) to estimate in real time the seismic moment, the moment magnitude and the corner frequency of events recorded by the velocity sensors, using spectral analysis applied to S waves. The purpose of this work consists mainly in the estimation of the source parameters (Mw, M0 and f0), combined with ground motion parameters using the velocity network of the NIEP, of more than 70 automatically located seismic events with Mw magnitude between 2.7 and 5.4. The main goals are the independent estimation of the seismic moment and common characterization for all events recorded by the velocity network.