The main goal of this paper is to give the basis for creating a computer-based clinical decision support (CDS) system for laryngopathies. One of approaches which can be used in the proposed CDS is based on the speech signal analysis using recurrent neural networks (RNNs). RNNs can be used for pattern recognition in time series data due to their ability of memorizing some information from the past. The Elman networks (ENs) are a classical representative of RNNs. To improve learning ability of ENs, we may modify and combine them with another kind of RNNs, namely, with the Jordan networks. The modified Elman-Jordan networks (EJNs) manifest a faster and more exact achievement of the target pattern. Validation experiments were carried out on speech signals of patients from the control group and with two kinds of laryngopathies.

The research concerns computer-based clinical decision support for laryngopathies. The proposed computer tool is based on a speech signal analysis in the time domain using recurrent neural networks. Such networks have the ability of time series prediction because of their memory nodes as well as local recurrent connections. In our experiments we use the modified Elman-Jordan neural network. For this kind of the net, we can observe acceleration of a learning process and that is important in real-time decision making.

Lack of sleep is a factor that disrupts the receptors’ reception of information from the environment and contributes to the emergence of problems with maintaining balance. The main aim of the study was to determine whether sleep disorders affect postural stability in young men. The study participants were 76 male students who were divided into groups with good and poor sleep quality. The division was made based on the results obtained from the questionnaire of the Pittsburgh Sleep Quality Index (PSQI). In each group, postural stability had been tested using three main tests: Sensory Organization Test (SOT); Motor Control Test (MCT); and Adaptation Test (ADT). The results of the analysis show that the obtained results differ in the examined groups under the SOT test. Different values of the tested parameters were noted among people with poor sleep quality and compared with the values of those who sleep well, which translates into a difference in the ability to maintain balance. The greatest impact is observed when using visual and a vestibular system to maintain a stable posture. It was confirmed that the lack of sleep significantly disturbs postural stability.

In the paper, we focus on ant-based clustering time series data represented by means of the so-called delta episode information systems. A clustering process is made on the basis of delta representation of time series, i.e., we are interested in characters of changes between two consecutive data points in time series instead of original data points. Most algorithms use similarity measures to compare time series. In the paper, we propose to use a measure based on temporal rough set flow graphs. This measure has a probabilistic character and it is considered in terms of the Decision Theoretic Rough Set (DTRS) model. To perform ant-based clustering, the algorithm based on the versions proposed by J. Deneubourg, E. Lumer and B. Faieta as well as J. Handl et al. is used.

Our research concerns data derived from the examined patient's speech signals for a non-invasive diagnosis of selected larynx diseases. The paper is devoted to the rule-based classification of patients on the basis of a family of coefficients reflecting spectrum disturbances around basic tones and their multiples. The paper presents a proposed procedure for feature selection and classification as well as the experiments carried out on real-life data.