This paper presents an effective solution of packetizing of non-uniform data for the purpose of reporting in telemedical surveillance systems with adaptive interpretation. Unlike the regular systems, where the data continuity is guaranteed by the common reporting interval and unified report content, the adaptive systems must implement a reservation procedure in order to proper data delivery, accordingly to sampling rates set individually for each of the diagnostic parameters. This procedure combines the content of every packet with respect to changes in data flow from particular diagnostic data, caused by time-variable requirements for the update rate of their time series. Our approach postulates appending to the information structure of two auxiliary data attributes, the validity period and the priority. The proposed solution was implemented and tested in a prototype cardiology-oriented monitor using two alternative reporting modes following the monitoring requisites. In immediate mode diagnostic packets are transmitted immediately accordingly to the time requirements, what allows the telediagnostic system to respond in short time in case of emergency. In delayed mode the transmission is deferred until packets are entirely filled with valid data, what limits the usage of data carrier for long-time regular reporting.

The aim of the study was analysis of histograms of selected sequences of EEG maps to assess variability of isopotential areas. In clinical practice this variability is evaluated using visual inspection that is subjective and may be difficult in revealing of subtle differences. Variability of isopotential areas is manifested by changes of isopotential areas as well as changes of their topolocalisation. The histograms of total map areas include the information concerning the ispotential area variability, while histogram analysis of particular areas of map may be useful for evaluation of variability of isopotential areas configuration. Basing on the examples containing the period before, during and after generalized seizure activity, the variability of constellations of isopotential areas in EEG maps and related to that diversity of their histogram types were presented. The work includes examples of series of EEG map histograms with their statistical analysis and the description of method used for the quantitative assessment of the histogram series variability for the maps sequences containing seizure activity.

In this paper we discuss applications of pattern recognition and image processing to automatic processing and analysis of histopathological images. We focus on counting of Red and White blood cells using microscopic images of blood smear samples. We provide literature survey and point out new challenges. We present an improved cell counting algorithm.

The paper presents a hypertension type classification task where the decisions should be made only on the basis of blood pressure, general information and basis biochemical data. This problem has a great importance to the medical decision support systems, yet results achieved so far are not satisfactory. When the canonical approaches tend to fail we should look for the compound pattern recognition systems, such as multiple classifiers systems. This article presents the results of an experimental investigation of the pool of compound classifiers which have their origin in classifiers ensembles, random forest, and random subspace. Presented methods returned good, satisfactory results, outperforming canonical approaches for this problem.

Abdominal Aortic Aneurysm (AAA) is a local dilation of the Aorta that occurs between the renal and iliac arteries. Recently developed treatment involves the insertion of a endovascular prosthetic (EVAR), which has the advantage of being a minimally invasive procedure but also requires monitoring to analyze postoperative patient outcomes. The most widespread method for monitoring is computerized axial tomography (CAT) imaging, which allows 3D reconstructions and segmentations of the aorta’s lumen of the patient under study. Previously published methods measure the deformation of the aorta between two studies of the same patient using image registration techniques. This paper applies neural network and statistical classifiers to build a predictor of patient survival. The features used for classification are the volume registration quality measures after each of the image registration steps. This system provides the medical team an additional decision support tool.

The cutaneous recording of gastric myoelectrical activity of the stomach known as electrogastrography (EGG) seems to be the promising tool for the non-invasive assessment of gastric motility. Unfortunately the EGG recording is usually severely contaminated both by motion artefacts and endogenous biological noise source. In order to use EGG signals as reliable diagnostic tool it is necessity to look for the effective artefacts removal methods. In this paper Continuous Wavelet Transform (CWT) was applied for detection motion artefacts from the EGG data. The set of own mother wavelets extracted directly from EGG signal was created and applied for detecting motion artefacts from one channel EGG recording. The results was compared with the effects obtained by using standard mother wavelets. The proposed method based on CWT with own mother wavelet presents very good performance for detecting motion artefacts from the EGG data.

The goal of the paper is to present different approaches to privacy-preserving data sharing and publishing in the context of e-health care systems. In particular, the literature review on technical issues in privacy assurance and current real-life high complexity implementation of medical system that assumes proper data sharing mechanisms are presented in the paper.

Hybrid patient classification system in nursing logistics activities is discussed in this paper. Hybrid classification model is based on two of the most used competitive artificial neural network algorithms that use learning vector quantization models (LVQ) and self-organizing maps (SOM). In general, the history of patient classification in nursing dates back to the period of Florence Nightingale. The first and the foremost condition for providing quality nursing care, which is measured by care standards, and determined by number of hours of actual care, is the appropriate number of nurses. It is possible to discus three types of experimental results. First result type could be assessment for risk of falling measured by Mors scale and pressure sores risk measured by Braden scale. Both of them are assessed by LVQ. Hybrid LVQ-SOM model is used for second result type, which presents the time for nursing logistics activities. The third type is possibility to predict appropriate number of nurses for providing quality nursing care. This research was conducted on patients from Institute of Neurology, Clinical Centre of Vojvodina.

In the paper the concept of a training system is presented which can help to stimulate sensory-motor cortex centers in order to develop their ability for efficient use of bioprosthesis. The basis of the training system is a virtual reality with a virtual hand, that the trained patient can move and concurrently observe the movement on the screen (visual feedback) and whose contact with virtual objects the patient may feel as a touch (sensory feedback). The construction of the virtual hand consists of physical elements, connected by joints, a graphical object representing the structure of the hand and the bones enable its deformation. The control procedure of virtual hand is realized through recognition of intention of hand motion on the basis of EMG signals coming from the stump muscles. The recognition algorithm is constructed using the learning set, i.e the set of pairs containing the class of hand fingers movement and accompanying myopotentials segments, which are acquired from the muscles of healthy hand.