Depression Episodes Detection: A Neural Netand Deep Neural Net Comparison.

Depression is a frequent mental disorder. It is estimated thatit affects more than 300 million people in the world. In this investiga-tion, a motor activity database was used, from which the readings of 55patients (32 control patients and 23 patients with the condition) wereselected, during one week in one minute intervals, obtaining a total of385 observations (participants) and 1440 characteristics (time intervals)from which the most representative one minute intervals were extractedapplying genetic algorithms that reduced the number of data to process,with this strategy it is guaranteed that the most representative genes(characteristics) in the chromosome population is included in a singlemachine learning model of which applied deep neural nets and neuralnets with the aim of creating a comparative between the models gener-ated and determining which model offers better performance to detectingepisodes of depression. The deep neural networks obtained the best per-formance with 0.8086 which is equivalent to 80.86 % of precision, thisdeep neural network was trained with 270 of the participants which isequivalent to 70 % of the observations and was tested with 30 % Remain-ing data which is equal to 115 participants of which 53 were diagnosedas healthy and 40 with depression correctly. Based on these results, itcan be concluded that the implementation of these models in smart de-vices or in some assisted diagnostic tool, it is possible to perform theautomated detection of episodes of depression reliably.La depresión es un trastorno mental frecuente. Se estima que afecta a más de 300 millones de personas en el mundo. En esta investigación se utilizó una base de datos de actividad motora, de la cual se seleccionaron las lecturas de 55 pacientes (32 pacientes control y 23 pacientes con la condición), durante una semana en intervalos de un minuto, obteniendo un total de 385 observaciones (participantes) y 1440 características (intervalos de tiempo) de los cuales se extrajeron los intervalos de un minuto más representativos aplicando algoritmos genéticos que redujeron el número de datos a procesar, con esta estrategia se garantiza que los genes (características) más representativos de la población cromosómica se incluyan en un aprendizaje de una sola máquina modelo del cual se aplicó redes neuronales profundas y redes neuronales con el objetivo de crear una comparativa entre los modelos generados y determinar qué modelo ofrece mejor desempeño para detectar episodios de depresión. Las redes neuronales profundas obtuvieron el mejor desempeño con 0.8086 lo que equivale al 80.86% de precisión, esta red neuronal profunda fue entrenada con 270 de los participantes que es equivalente al 70% de las observaciones y se probó con el 30% de los datos restantes que es igual a 115 participantes de los cuales 53 fueron diagnosticados como sanos y 40 con depresión correctamente. En base a estos resultados, se puede concluir que la implementación de estos modelos en dispositivos inteligentes o en alguna herramienta de diagnóstico asistido, es posible realizar la detección automatizada de episodios de depresión de manera confiable

Bipolar disorder: evolution of the concept and current controversies

The author reviews the evolution of the concept of bipolar disorder as an ongoing process. Its roots can be found in the work of Araeteus of Capadocia, who assumed that melancholia and mania were two forms of the same disease. The modern understanding of bipolar disorder began in France, through the work of Falret (1851) and Baillarger (1854). The pivotal concepts of Emil Kraepelin changed the basis of psychiatric nosology, and Kraepelin's unitary concept of manic-depressive insanity was largely accepted. Kraepelin and Weigandt's ideas on mixed states were the cornerstone of this unitary concept. After Kraepelin, however, the ideas of Kleist and Leonhard, in Germany, as well as the work of Angst, Perris and Winokur, emphasized the distinction between unipolar and bipolar forms of depression. More recently, the emphasis has shifted again to the bipolar spectrum, which, in its mild forms, expanded to the limits of normal temperament. In concluding, the author summarizes the polemic aspects concerning the nosology of bipolar disorder and its boundaries in comparison with those of with schizophrenia, schizoaffective disorders and cycloid psychosisO autor revê o conceito de transtorno bipolar como um processo em evolução. Suas raízes podem ser encontradas no trabalho de Araeteus da Capadócia, que assumia serem a melancolia e a mania duas formas da mesma doença. A compreensão atual da doença bipolar começou na França, através dos trabalhos de Falret (1851) e Baillarguer (1854). Os conceitos fundamentais de Kraepelin mudaram as bases da nosologia psiquiátrica, e o conceito unitário de Kraepelin sobre a insanidade maníaco-depressiva passou a ser amplamente aceito. Depois de Kraepelin, no entanto, as idéias de Kleist e Leonhard, na Alemanha, e o trabalho subseqüente de Angst, Perris e Winokur enfatizaram a distinção entre as formas monopolares e bipolares da depressão. Mais recentemente a ênfase mudou novamente para o espectro bipolar, que em suas formas leves expande-se às bordas dos temperamentos normais. Finalizando, o autor sumariza os aspectos polêmicos da nosologia da doença bipolar e seus limites com as esquizofrenias, a doença esquizoafetiva e as psicoses ciclóides.Universidade Federal de São Paulo (UNIFESP), Escola Paulista de Medicina (EPM) da Universidade FederalUNIFESP, EPM, da Universidade FederalSciEL

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.Comment: 29 pages,2 figures and 18 Table

Blue-blocking glasses as adjunctive treatment for bipolar mania - and exploration of motor activity patterns in serious mental disorders

Background: There is a need for more effective treatments of bipolar mania. Promising reports of the effects of dark therapy on bipolar disorder symptoms and the discovery of a mainly blue-light sensitive daylight-signaling retinal ganglion cells has resulted in the utility of BB glasses to create a virtual darkness condition for the brain. Changes in activation or aberrant motor activity is present in all serious mental disorders. Actigraphy is a non-invasive and simple means of assessing motor activity, but is still mostly used to assess sleep outcomes. Before the utility of actigraphy can be broadened, there is need for further exploration of daily activity pattern characteristics for the diagnostic entities. Aims: By means of the Virtual Darkness as Additive Treatment in Mania (VATMAN) trial, we aimed to test the effectiveness and feasibility of BB glasses as an adjunctive treatment for mania compared to placebo glasses. As part of the Agitation at Admittance to a Psychiatric Acute Department Study, we aimed to characterize the motor activity patterns among a new sample of patients with psychotic disorders, and compare these characteristics to the motor activity patterns of patients with affective disorders and with healthy controls. Methods: Eligible patients for the VATMAN trial (hospitalized with bipolar disorder mania and otherwise fulfilling inclusion criteria) were randomized to receive either BB-glasses or clear-lensed placebo glasses. The glasses were worn as an adjunctive treatment from 6:00 p.m. to 8:00 a.m. for seven consecutive days. Manic symptoms were rated daily using the Young Mania Rating Scale. Motor activity was measured using wrist-worn actigraphs. Feasibility was assessed using a self-report patient experience questionnaire together with the clinical observation of side-effects. Sleep was assessed using actigraphy-derived sleep parameters. In the Agitation at Admittance to a Psychiatric Acute Department study, all hospitalized patients in the acute psychiatric ward in Østmarka Hospital, Trondheim were asked to wear an actigraph for 24 h. The motor activity patterns of patients diagnosed with schizophrenia and other psychotic disorders were compared to those of patients with mania, motor-retarded unipolar depression, and healthy controls. Linear and non-linear analytical methods were used to describe and compare motor activity variability and complexity (irregularity) for a 24 h period as well as in morning and evening sequences. Results: Out of 32 randomized patients in the VATMAN trial, 12 patients in the BB-group and 11 patients in the placebo-group were included in the analyses. After seven days, the Cohen’s d effect size was 1.86. There was a significant group difference in YMRS scores after three days (p = 0.042) and the group difference increased steadily throughout the intervention. Observed side effects included headache in one patient and rapidly reversible depressive symptoms in two patients. Actigraphy-derived sleep outcomes at night five showed significantly higher sleep efficiency, lower motor activity and less minutes of wake after sleep onset in the BB group as compared to the placebo group. Several patients in both groups displayed a 48 h-like rhythm of shorter or disrupted sleep. The schizophrenia spectrum group shared the characteristic of high motor activity variability with the unipolar depressed group, but differed with respect to more irregular (complex) activity pattern in the morning sequence. The schizophrenia spectrum and the mania groups could not be separated using formal statistical analyses, being most similar with regards to high morning activity irregularity. The mania group was the only one to show a blunted morning-to-evening activity fluctuation, while the normal morning-to-evening decline was more preserved in the schizophrenia spectrum group. Conclusions: BB-glasses were found to be both effective and feasible as an adjunctive treatment for mania. The BB-group showed actigraphy-derived sleep parameters reflecting less activated sleep compared with the placebo-group. The use of actigraphy data to characterize diurnal motor activity patterns, by use of the combination of linear and non-linear analytical approaches, seems to have potential for assessment of symptoms and for diagnostic support

Motor activity patterns in acute schizophrenia and other psychotic disorders can be differentiated from bipolar mania and unipolar depression

Under embargo until: 02.10.2019The purpose of this study was to compare 24-h motor activity patterns between and within three groups of acutely admitted inpatients with schizophrenia and psychotic disorders (n = 28), bipolar mania (n = 18) and motor-retarded unipolar depression (n = 25) and one group of non-hospitalized healthy individuals (n = 28). Motor activity was measured by wrist actigraphy, and analytical approaches using linear and non-linear variability and irregularity measures were undertaken. In between-group comparisons, the schizophrenia group showed more irregular activity patterns than depression cases and healthy individuals. The schizophrenia and mania cases were clinically similar with respect to high prevalence of psychotic symptoms. Although they could not be separated by a formal statistical test, the schizophrenia cases showed more normal amplitudes in morning to evening mean activity and activity variability. Schizophrenia constituted an independent entity in terms of motor activation that could be distinguished from the other diagnostic groups of psychotic and non-psychotic affective disorders. Despite limitations such as small subgroups, short recordings and confounding effects of medication/hospitalization, these results suggest that detailed temporal analysis of motor activity patterns can identify similarities and differences between prevalent functional psychiatric disorders. For this purpose, irregularity measures seem particularly useful to characterize psychotic symptoms and should be explored in larger samples with longer-term recordings, while searching for underlying mechanisms of motor activity disturbances.acceptedVersio

Circuits regulating pleasure and happiness in bipolar disorder

According to our model, the motivation for appetitive-searching vs. distress-avoiding behaviors is regulated by two parallel cortico-striato-thalamo-cortical (CSTC) re-entry circuits that include the core and the shell parts of the nucleus accumbens, respectively. An entire series of basal ganglia, running from the caudate nucleus on one side to the centromedial amygdala on the other side, control the intensity of these reward-seeking and misery-fleeing behaviors by stimulating the activity of the (pre)frontal and limbic cortices. Hyperactive motivation to display behavior that potentially results in reward induces feelings of hankering (relief leads to pleasure); while, hyperactive motivation to exhibit behavior related to avoidance of aversive states results in dysphoria (relief leads to happiness). These two systems collaborate in a reciprocal fashion. We hypothesized that the mechanism inducing the switch from bipolar depression to mania is the most essential characteristic of bipolar disorder. This switch is attributed to a dysfunction of the lateral habenula, which regulates the activity of midbrain centers, including the dopaminergic ventral tegmental area (VTA). Froman evolutionary perspective, the activity of the lateral habenula should be regulated by the human homolog of the habenula-projecting globus pallidus, which in turn might be directed by the amygdaloid complex and the phylogenetically old part of the limbic cortex. In bipolar disorder, it is possible that the system regulating the activity of this reward-driven behavior is damaged or the interaction between the medial and lateral habenula may be dysfunctional. This may lead to an adverse coupling between the activities of the misery-fleeing and reward-seeking circuits, which results in independently varying activities

Depression Episodes Detection in Unipolar and Bipolar Patients: A Methodology with Feature Extraction and Feature Selection with Genetic Algorithms Using Activity Motion Signal …

Depression is a mental disorder which typically includes recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, and in severe cases fatigue, causing inability to perform daily activities, leading to a progressive loss of quality of life. Monitoring depression (unipolar and bipolar patients) stats relays on traditional method reports from patients; however, bias is commonly present, given the patients’ interpretation of the experiences. Nevertheless, to overcome this problem, Ecological Momentary Assessment (EMA) reports have been proposed and widely used. These reports includes data of the behaviour, feelings, and other type of activities recorded almost in real time using different types of portable devices, which nowadays include smartphones and other wearables such as smartwatches. In this study is proposed a methodology to detect depressive patients with the motion data generated by patient activity, recorded with a smartband, obtained from the “Depresjon” database. Using this signal as information source, a feature extraction approach of statistical features, in time and spectral evolution of the signal, is done. Subsequently, a clever feature selection with a genetic algorithm approach is done to reduce the amount of information required to give a fast noninvasive diagnostic. Results show that the feature extraction approach can achieve a value of 0.734 of area under the curve (AUC), and after applying feature selection approach, a model comprised by two features from the motion signal can achieve a 0.647 AUC. These results allow us to conclude that using the activity signal from a smartband, it is possibl

Repetitive transcranial magnetic stimulation in bipolar depression: Another puzzle of manic switch?

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The emerging modern face of mood disorders: a didactic editorial with a detailed presentation of data and definitions

The present work represents a detailed description of our current understanding and knowledge of the epidemiology, etiopathogenesis and clinical manifestations of mood disorders, their comorbidity and overlap, and the effect of variables such as gender and age. This review article is largely based on the 'Mood disorders' chapter of the Wikibooks Textbook of Psychiatry http://en.wikibooks.org/wiki/Textbook_of_Psychiatry/Mood_Disorders