Major Depression Disease has been increasing in the last few years, affecting around 7 percent of the world population, but nowadays techniques to diagnose it are outdated and inefficient. Motor activity data in the last decade is presented as a better way to diagnose, treat and monitor patients suffering from this illness, this is achieved through the use of machine learning algorithms. Disturbances in the circadian rhythm of mental illness patients increase the effectiveness of the data mining process. In this paper, a comparison of motor activity data from the night, day and full day is carried out through a data mining process using the Random Forest classifier to identified depressive and non-depressive episodes. Data from Depressjon dataset is split into three different subsets and 24 features in time and frequency domain are extracted to select the best model to be used in the classification of depression episodes. The results showed that the best dataset and model to realize the classification of depressive episodes is the night motor activity data with 99.37% of sensitivity and 99.91% of specificity.

Major depressive disorder (MDD) is the most recurrent mental illness globally, affecting approximately 5% of adults. Furthermore, according to the National Institute of Mental Health (NIMH) of the U.S., calculating an actual schizophrenia prevalence rate is challenging because of this illness’s underdiagnosis. Still, most current global metrics hover between 0.33% and 0.75%. Machine-learning scientists use data from diverse sources to analyze, classify, or predict to improve the psychiatric attention, diagnosis, and treatment of MDD, schizophrenia, and other psychiatric conditions. Motor activity data are gaining popularity in mental illness diagnosis assistance because they are a cost-effective and noninvasive method. In the knowledge discovery in databases (KDD) framework, a model to classify depressive and schizophrenic patients from healthy controls is constructed using accelerometer data. Taking advantage of the multiple sleep disorders caused by mental disorders, the main objective is to increase the model’s accuracy by employing only data from night-time activity. To compare the classification between the stages of the day and improve the accuracy of the classification, the total activity signal was cut into hourly time lapses and then grouped into subdatasets depending on the phases of the day: morning (06:00–11:59), afternoon (12:00–17:59), evening (18:00–23:59), and night (00:00–05:59). Random forest classifier (RFC) is the algorithm proposed for multiclass classification, and it uses accuracy, recall, precision, the Matthews correlation coefficient, and F1 score to measure its efficiency. The best model was night-featured data and RFC, with 98% accuracy for the classification of three classes. The effectiveness of this experiment leads to less monitoring time for patients, reducing stress and anxiety, producing more efficient models, using wearables, and increasing the amount of data.

Mental disorders like depression represent 28% of global disability. It affects around 7.5% percent of global disability. Depression is a common disorder that affects the state of mind, normal activities, emotions, and produces sleep disorders. It is estimated that approximately 50% of depressive patients suffering from sleep disturbances. In this paper, a data mining process to classify depressive and not depressive episodes during nighttime is carried out based on a formal method of data mining called Knowledge Discovery in Databases (KDD). KDD guides the process of data mining with stages well established: Pre-KDD, Selection, Pre-processing, Transformation, Data Mining, Evaluation, and Post-KDD. The dataset used for the classification is the DEPRESJON dataset, which contains the motor activity of 23 unipolar and bipolar depressed patients and 32 healthy controls. The classification is carried out with two different approaches; a multivariate and univariate analysis to classify depressive and non-depressive episodes. For the multivariate analysis, the Random Forest algorithm is implemented with a model construct of 8 features and, results of the classification are; specificity equal to 0.9927 and sensitivity equal to 0.9991. The univariate analysis shows that the maximum in time of the activity is the most descriptive characteristic of the model with 0.908 in accuracy for the classification of depressive episodes.

The simulation based on mannequins for medical education has been used for a few decades and has given favorable results in the comprehensive development of students in the area of Health Care Sciences, in this article an investigation is made about the benefits of this technique and a way to improve it, to reach it a high fidelity simulator with a torso dummy was developed for teaching cardiopulmonary auscultation. The proposed functional prototype was developed using an Internet of Things approach in which a mannequin torso is controlled through a mobile application for the reproduction of heart and lung sounds from forty different pathologies, the brain of the simulator is a micro-controller that stores the sounds and has the server functionality for the connection with the mobile application through WiFi. The resulting product is a high fidelity simulator with low cost, usable and accessible for students and institutions.

Психические расстройства типа депрессии составляют 28% от общего числа заболеваний, связанных с инвалидностью, и около 7,5% инвалидов в мире имеют подобные расстройства. Депрессия – это распространенное расстройство, которое влияет на душевное состояние, ежедневную деятельность, эмоции, а также вызывает нарушения сна. По некоторым оценкам, примерно 50% пациентов с депрессией страдают от нарушений сна. В данной работе процесс извлечения данных для классификации депрессивных и недепрессивных эпизодов в ночное время осуществляется на основе формального метода обнаружения знаний в базах данных (Knowledge Discovery in Databases, KDD). При использовании KDD процесс интеллектуального анализа данных имеет следующие четко выраженные этапы: предварительное обнаружение знаний в базах данных, отбор, предварительная обработка, преобразование, собственно анализ, оценка и последующее обнаружение знаний в базах данных. Для классификации был использован набор данных DEPRESJON, в котором содержится информация о двигательной активности 23 пациентов с монополярным и биполярным расстройством и 32 здоровых людей. Для классификации депрессивных и недепрессивных эпизодов были использованы два различных метода – многомерный и одномерный анализ данных. Для многомерного анализа применяется алгоритм случайного леса с моделью, основанной на 8 признаках. Результаты классификации имеют специфичность 0,9927 и чувствительность 0,9991. Одномерный анализ показывает, что наиболее информативной характеристикой модели являются пики активности, обеспечивающие точность 0,908 при классификации депрессивных эпизодов.