Band 16, Heft 1

This article contains the description of a web application created in order to help children and adolescents with intellectual disabilities. The application gathers simple serious games that are supposed to be an addition to the daily therapy, widening and intensifying its results. The article starts with a quick review of already created serious games for people with the autism spectrum and the Down syndrome. Later, it focuses on formal and technical details of the chosen solution. The main part of the publication contains descriptions of the particular games created for the application. The article explains the rules of each game and depicts it with a screenshot from the application. Finally, the reader can find the results of the tests conducted with the help of the parents of pupils attending one of Cracow’s special needs school. The tests, during which the children were encouraged to use the web application in their homes, lasted for a little more than 3 weeks. The final section also contains opinions of the therapists with more than 20 years of experience.

This paper aims to propose a deep learning model based on big data for the healthcare system to predict social network data. Social network users post large amounts of healthcare information on a daily basis and at the same time hospitals and medical laboratories store very large amounts of healthcare data, such as X-rays. The authors provide an architecture that can integrate deep learning, social networks, and big data. Deep learning is one of the most challenging areas of research and is becoming increasingly popular in the health sector. It uses deep analysis to extract knowledge with optimum precision. The proposed architecture consists of three layers: the deep learning layer, the big data layer, and the social networks layer. The big data layer includes data for health care, such as X-ray images. For the deep learning layer, three Convolution Neuronal Network models are proposed for X-ray image classification. As a result, social network layer users can access the proposed system to predict their X-ray image posts.

Despite great progress in understanding the functions and structures of the central nervous system (CNS) the brain stem remains one of the least understood systems. We know that the brain stem acts as a decision station preparing the organism to act in a specific way, but such functions are rather difficult to model with sufficient precision to replicate experimental data due to the scarcity of data and complexity of large-scale simulations of brain stem structures. The approach proposed in this article retains some ideas of previous models, and provides more precise computational realization that enables qualitative interpretation of the functions played by different network states. Simulations are aimed primarily at the investigation of general switching mechanisms which may be executed in brain stem neural networks, as far as studying how the aforementioned mechanisms depend on basic neural network features: basic ionic channels, accommodation, and the influence of noise.

Heart sounds play a crucial role in the clinical assessment of patients. Stethoscopes are used for detecting heart sounds and diagnosing potential abnormal conditions. However, several parameters of the cardiac sounds cannot be extracted by traditional stethoscopes. This paper presents a proposed algorithm based on peaks detection. Besides its ability of filtering the heart sounds signals, the time intervals of these sounds in addition to the heart rate were calculated by the proposed algorithm in an efficient way. Signals of the heart sounds from two sources were used to evaluate the efficiency of the algorithm. The first source was the data recorded from 14 participants, whereas the second source was the free data set sponsored by PASCAL. The algorithm showed different performance accuracy for detecting the main heart sounds based on the source of the data used in the study. The accuracy was 93.6% when using the data recorded from the first source, whereas it was 76.194% for the data of the second source.

The electrocardiogram (ECG) morphology determines the overall activity of the heart and is the most widely used tool in the diagnostic processes. T wave is a crucial wave component that reveals very useful information regarding various cardiac disorders. In this paper we have proposed a novel T wave detection technique based on adaptive window and simple decision rule. The proposed technique uses two-stage median filters followed by the Savitzky-Golay filter at the pre-processing stage to remove the noises in the ECG signal. The QRS complex is detected for locating the T wave as a reference in one ECG cycle. An R-R interval based window is considered for detecting the T wave, and decision logic depends on the iso-electric line value. The proposed technique is tested on the QT database and self-recorded dataset for its performance evaluation. In the present work, the results achieved for T wave detection sensitivity (Se), positive predictivity (+P), detection error rate (DER), and accuracy (Acc) on the QT database are Se = 97.57%, +P = 99.63%, DER = 2.78%, and Acc = 97.22% with an average time error of (3.468 ± 5.732) ms. The proposed technique shows Se = 99.94%, +P = 99.94%, DER = 0.01%, and Acc = 99.89% on the self-recorded dataset. The proposed technique is also capable of detecting both the upward and downward T wave efficiently in the ECG signal.

The behavioral pattern of children with special needs depends on their emotional and developmental disability. Any abnormal and incorrect pattern of behavior which is below the level of development as the expected norm can be considered as “the challenging behavior.” For supporting children with behavioral problems, many interventions and strategies alone, or in combination, are used. Behavioral and developmental problems, if not treated well, in childhood, may cause a problem and have negative long-term and short-term effects on a child’s personal life, education, family, and professional life. Detailed knowledge about the child’s behavior is important to define the problem. Hence, the software contains the questionnaire pattern, which is divided into categories like parents, teachers, doctors, and friends. Child behavior checklist is used to define the questionnaire. Images and video analysis are used to detect the current emotion in the child. By analyzing the behavioral pattern and current emotion, the teaching method will be suggested.