Feature Extraction in Motor Activity Signal: Towards a Depression Episodes Detection in Unipolar and Bipolar Patients.

Depression is a mental disorder characterized by recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, in addition to fatigue, causing inability to perform daily activities, which leads to a loss of quality of life. To monitor depression (unipolar and bipolar patients), traditional methods rely on reports from patients; nevertheless, bias is commonly present in them. To overcome this problem, Ecological Momentary Assessment (EMA) reports have been widely used, which include data of the behavior, feelings and other types of activities recorded almost in real time through the use of portable devices and smartphones containing motion sensors. In this work a methodology was proposed to detect depressive subjects from control subjects based in the data of their motor activity, recorded by a wearable device, obtained from the "Depresjon" database. From the motor activity signals, the extraction of statistical features was carried out to subsequently feed a random forest classifier. Results show a sensitivity value of 0.867, referring that those subjects with presence of depression have a degree of 86.7% of being correctly classified, while the specificity shows a value of 0.919, referring that those subjects with absence of depression have a degree of 91.9% of being classified with a correct response, using the motor activity signal provided from the wearable device. Based on these results, it is concluded that the motor activity allows distinguishing between the two classes, providing a preliminary and automated tool to specialists for the diagnosis of depression.

Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013⁻2014.

Oral health represents an essential component in the quality of life of people, being a determinant factor in general health since it may affect the risk of suffering other conditions, such as chronic diseases. Oral diseases have become one of the main public health problems, where dental caries is the condition that most affects oral health worldwide, occurring in about 90% of the global population. This condition has been considered a challenge because of its high prevalence, besides being a chronic but preventable disease which can be caused depending on the consumption of certain nutritional elements interacting simultaneously with different factors, such as socioeconomic factors. Based on this problem, an analysis of a set of 189 dietary and demographic determinants is performed in this work, in order to find the relationship between these factors and the oral situation of a set of subjects. The oral situation refers to the presence and absence/restorations of caries. The methodology is performed constructing a dense artificial neural network (ANN), as a computer-aided diagnosis tool, looking for a generalized model that allows for classifying subjects. As validation, the classification model was evaluated through a statistical analysis based on a cross validation, calculating the accuracy, loss function, receiving operating characteristic (ROC) curve and area under the curve (AUC) parameters. The results obtained were statistically significant, obtaining an accuracy ≃ 0.69 and AUC values of 0.69 and 0.75. Based on these results, it is possible to conclude that the classification model developed through the deep ANN is able to classify subjects with absence of caries from subjects with presence or restorations with high accuracy, according to their demographic and dietary factors.

A Case⁻Control Study of Socio-Economic and Nutritional Characteristics as Determinants of Dental Caries in Different Age Groups, Considered as Public Health Problem: Data from NHANES 2013⁻2014.

One of the principal conditions that affects oral health worldwide is dental caries, occurring in about 90% of the global population. This pathology has been considered a challenge because of its high prevalence, besides being a chronic but preventable disease which can be caused by a series of different demographic, dietary" among others. Based on this problem, in this research a demographic and dietary features analysis is performed for the classification of subjects according to their oral health status based on caries, according to the age group where the population belongs, using as feature selector a technique based on fast backward selection (FBS) approach for the development of three predictive models, one for each age range (group 1: 10⁻19; group 2: 20⁻59; group 3: 60 or more years old). As validation, a net reclassification improvement (NRI), AUC, ROC, and OR values are used to evaluate their classification accuracy. We analyzed 189 demographic and dietary features from National Health and Nutrition Examination Survey (NHANES) 2013⁻2014. Each model obtained statistically significant results for most features and narrow OR confidence intervals. Age group 2 obtained a mean NRI = -0.080 and AUC = 0.933; age group 3 obtained a mean NRI = -0.024 and AUC = 0.787; and age group 4 obtained a mean NRI = -0.129 and AUC = 0.735. Based on these results, it is concluded that these specific demographic and dietary features are significant determinants for estimating the oral health status in patients based on their likelihood of developing caries, and the age group could imply different risk factors for subjects.