Hubert Wojtowicz

Intelligent information system for interpretation of dermatoglyphic patterns of down's syndrome in infants

The paper describes design of an intelligent information system for assessment of dermatoglyphic indices of Downs syndrome in infants. The system supports medical diagnosis by automatic processing of dermatoglyphic prints and detecting features indicating presence of genetic disorders. Application of image processing and pattern recognition algorithms in pattern classification of fingerprints and prints of hallucal area of the sole is described. Application of an algorithm based on multi-scale pyramid decomposition of an image is proposed for ridge orientation calculation. A method of singular points detection and calculation of ATD angle of the palm print is presented. Currently achieved results in dermatoglyphic prints enhancement, classification and analysis are discussed. Scheme used in classification of dermatoglyphic prints is described. RBF and triangular kernel types are used in the training of SVM multi-class systems generated with one-vs-one scheme. Results of experiments conducted on the database of Collegium Medicum of the Jagiellonian University in Cracow are presented.

Artificial immune system for medical data classification

The article presents application of artificial immune algorithms in classification of vectorized medical data sets. Artificial immune network was created and trained for the purpose of arterial blood gasometry parameters (pH, pCO2, pO2, HCO3) classification. Training data originates from the Infant Intensive Care Unit of the Polish – American Institute of Pediatry, Collegium Medicum, Jagiellonian University in Cracow.

Medical Decision Support System for Assessment of Dermatoglyphic Indices and Diagnosis of Down’s Syndrome

The paper describes design of an intelligent information system for assessment of dermatoglyphic indices of Down’s syndrome in infants. The system supports medical diagnosis by automatic processing of dermatoglyphic prints and detecting features indicating presence of genetic disorders. Application of image processing and pattern recognition algorithms in pattern classification of fingerprints and prints of the hallucal area of the sole is described. Application of an algorithm based on multi-scale pyramid decomposition of an image is proposed for ridge orientation calculation. A method of singular points detection and calculation of the ATD angle of the palm print is presented. Currently achieved results in dermatoglyphic prints enhancement, classification and analysis are discussed.

Simulation of Electrohydrodynamic Phenomenon using Computational Intelligence Methods

Abstract The electrohydrodynamic effect occurring during an underwater electrical explosion of a copper wire and method of its simulation using computational intelligence algorithms are presented in the paper. Principles of operation of the seismic wave EHD generator are explained. Differential equations describing a mathematical model of changes in a release circuit of the EHD generator are provided. The results of computations of differential equations, using Runge -Kutta algorithm, are provided. The simulation is based on current intensity and pressure data sets recorded during field experiments and data obtained by solving differential equations of the theoretical model. The relationships between electrical parameters of the electrohydrodynamic discharge circuit, material parameters of the exploded wire and result- ing pressure values of the shock wave are discussed. A regression model is created using computational intelligence algorithms for prediction of pressure values of a seismic wave resulting from the explosion of the wire. The results of the prediction of the pressure wave using neural networks are presented in the paper and verified by comparing the measured and calculated values of the pressure wave.

Analysis and synthesis of the system for processing of sign language gestures and translatation of mimic subcode in communication with deaf people

In the paper a design and principle of operation of the system facilitating communication between hearing and deaf people is presented. The system has a modular architecture and consists of main application, translation server and two complementary databases. The main application is responsible for interaction with the user and visualization of the sign language gestures. The translation server carries out translation of the text written in the Polish language to the appropriate messages of the sign language. The translation server is composed of facts database and translation rules implemented in the Prolog language. The facts database contains the set of the lexemes and their inflected forms with a description of the semantics of units. The translation rules carry out identification and analysis of basic structures of the Polish language sentence. These basic structures are related to the sentence creation function of the verb predicate. On the basis of this analysis equivalent translation of text into the sign language is realized. Translated text in the form of metadata is passed to the main application, where it is translated into the appropriate gestures of the sign language and face mimicry. The gestures in the form of 3d vectors and face mimicry are stored in the main database as binary objects. The authors intend to apply the translation system in various public institutions like hospitals, clinics, post offices, schools and offices.

Prediction of Arterial Blood Gases Values in Premature Infants with Respiratory Disorders

Arterial blood gases sampling (ABG) is a method for acquiring neonatal patients’ acid-base status. Variations of blood gasometry parameters values over time can be modelled using multi-layer artificial neural networks (ANNs). Accurate predictions of future levels of blood gases can be useful in supporting therapeutic decision making. In the paper several models of ANN are trained using growing numbers of feature vectors and assessment is made about the influence of input matrix size on the accuracy of ANNs’ prediction capabilities.

Electrohydrodynamic Effect Simulation and Method of Its Optimization

The paper presents an optimization method for electrohydrodynamic effect basing on results of its simulation. The EHD effect is simulated using neural networks and differential equations describing a mathematical model of the phenomenon, which are solved using Runge - Kutta algorithm. The optimization of this effect allows finding for particular input parameters of the generator an optimal diameter of the wire, which burned in a thermo-physical process gives a maximal energy release in the form of an acoustic pressure wave.

Bronchopulmonary Dysplasia Prediction Using Naive Bayes Classifier

The paper presents BPD (Bronchopulmonary Dysplasia) prediction for extremely premature infants after their first week of life. In contrast to the most works where LR (Logit Regression) is used, the naive Bayes classifier was proposed. Data was collected thanks to the Neonatal Intensive Care Unit of The Department of Pediatrics at Jagiellonian University Medical College and includes 109 patients with birth weight less than or equal to 1500 g. Fourteen different features were considered and all \(2^{14}\) of theirs combinations were analyzed. This paper also includes an accuracy and its deviation comparison with other prediction methods. It was possible because the calculations were performed on the very same data, which was used in previous works presenting LR and SVM forecasts.

Statistical Method for the Problem of Bronchopulmonary Dysplasia Classification in Pre-mature Infants

The problem of data classification with a statistical method is presented in the paper. Described classification method enables calculation of probability of disease incidence. A case of disease incidence is described with two parameters expressed in real numbers. The case can belong to a known set of cases where the disease occurred or to the set where the disease did not occur. A method for calculating probability with which a given case belongs to the set labeled as “1” or “0” is proposed. Source data used in the paper come from medical databases and are original. The algorithm of the method was checked on clinical cases. Correlation method was used for generating respective statistics. The calculated correlation at a level of 0.8 is indicative of disease occurrence, whereas the correlation coefficient at a level of 0.0 is indicative of the lack of disease. This property is used in the classification algorithm. It is frequent in the clinical practice that we have one test case and we try to determine whether or not that case describes symptoms of liability to the disease. Classification is related with the occurrence of Bronchopulmonary dysplasia, which is analyzed in a 3 to 4 week period preceding the disease incidence.

Arterial Blood Gases Forecast Optimization by Artificial Neural Network Method

Arterial blood gas sampling represents the gold standard method for acquiring patients’ acid-base status. It is proposed that blood gas values could be measured using arterialized earlobe blood samples. Pulse oximetry plus transcutaneous carbon dioxide measurement is an alternative method of obtaining similar information as well. Since dynamics of biochemical changes occurring in the blood is an individual feature which changes during the healing process authors proposed forecast models developed using artificial neural networks. The networks are trained with data vectors containing short term (72 h) history windows of four blood gasometry parameters. Several different optimization algorithms are used in the training phase to create a set of models from which the best prediction model is then selected.