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Information and communication technologies have already become inseparable part of healthcare sector activities. In the paper we discuss the issues of standardization and interoperability that are crucial for correct interconnec-tion of... more
Information and communication technologies have already become inseparable part of healthcare sector activities. In the paper we discuss the issues of standardization and interoperability that are crucial for correct interconnec-tion of medical and other devices and information systems. Our previous work in the area has led us to the conclusion that successful integration of partial solutions will be strongly de-pendent on the issue of interoperability of medical devices and information systems. It comprises problems of standardization of data acquisition, communication, processing, and storage; and connected problem: correct data mapping between differ-ent ICT applications.
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ABSTRACT The paper presents in its initial part fundamental mathematical tools for analysis of retinal images using methods of digital signal processing and enhancement and methods for their registration to monitor changes during the... more
ABSTRACT The paper presents in its initial part fundamental mathematical tools for analysis of retinal images using methods of digital signal processing and enhancement and methods for their registration to monitor changes during the treatment. Blood vessel trees are then extracted to analyse their specific structures to find retina disorders. The proposed graphical user interface is then used for analysis and monitoring of the set of 20 patients observed during the treatment using the optical coherence tomography. Features obtained are then analysed using selected statistical methods to compare features obtained and to propose the method for classification of healthy and diseased patients. The accuracy achieved was 79 %.
The most popular and publicly well-known are the studies using the Body Mass Index (BMI) as a single parameter indicating the degree of obesity (or slimness). However BMI cannot tell anything about the body composition. The Body Impedance... more
The most popular and publicly well-known are the studies using the Body Mass Index (BMI) as a single parameter indicating the degree of obesity (or slimness). However BMI cannot tell anything about the body composition. The Body Impedance Analysis (BIA) represents one of the methods for classification of body composition. As obesity does not refer to excessive body weight but it refers to the condition in which the individual has an excessive amount of body fat it is necessary to know distribution of fat tissue and fat-free mass in the body. The aim of the paper is to present results of our ongoing research focused on the influence of body hydration on the body impedance measurements and also on the influence of the frequency used for the measurement. The performed measurements showed certain influence which must be verified by repeated and extended experiments with greater number and variety of measured subjects.
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Formalization of the work practice using processes is a widely used method throughout various fields of industry. However, their application within the healthcare domain is rather unsuccessful due to the need for high agility, exceptions... more
Formalization of the work practice using processes is a widely used method throughout various fields of industry. However, their application within the healthcare domain is rather unsuccessful due to the need for high agility, exceptions handling, and for working with complex medical knowledge that affects these processes. To overcome these problems the agent paradigm and multi-agent systems can be applied. In this paper we present a novel architecture of a multi-agent system that is able to work with general processes. As an exemplary application of the architecture we describe a critiquing decision support system for healthcare specialists based on formalized medical guidelines.
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... Dynamic Time Warping The Derivative dynamic warping metrics (DDTW) is a modifi-cation of the classical dynamic time warping (DTW) algorithm The DTW algorithm technique allows comparison ... Figure 2 shows an example of time warping... more
... Dynamic Time Warping The Derivative dynamic warping metrics (DDTW) is a modifi-cation of the classical dynamic time warping (DTW) algorithm The DTW algorithm technique allows comparison ... Figure 2 shows an example of time warping measure applied to two ECG beats. ...
1 Dept. of Communications, Informatics and Management, TEI of Epirus, Kostakioi, Artas, Greece 2 Czech Technical University, Gerstner Laboratory, Prague, Czech Republic 3Department of Gynecology and Obstetrics, Porto Faculty of Medicine,... more
1 Dept. of Communications, Informatics and Management, TEI of Epirus, Kostakioi, Artas, Greece 2 Czech Technical University, Gerstner Laboratory, Prague, Czech Republic 3Department of Gynecology and Obstetrics, Porto Faculty of Medicine, Porto, Portugal
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A decision tree is a good classifier with a transparent decision mechanism. Decision-tree building methods usually have problems in splitting the learning samples into more subsets, because of the nature of the tree. If the classification... more
A decision tree is a good classifier with a transparent decision mechanism. Decision-tree building methods usually have problems in splitting the learning samples into more subsets, because of the nature of the tree. If the classification into such subsets is not possible, it is better to put the classification decision on to some other classifier. This leads to the introduction of a null classification, which simply means that no classification is possible in this step. This approach is sensible with evolutionary methods, as they can handle a number of trees simultaneously. In the process of construction, we have to address the problem of whether a classification is sensible. The performance of the proposed model has been tested on several data sets and the results presented on one such data set show its potential
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Measures from the theory of nonlinear dynamics were applied on complex fractionated atrial electrograms (CFAEs) in order to characterize their physiological dynamic behavior. The results were obtained considering 113 short term atrial... more
Measures from the theory of nonlinear dynamics were applied on complex fractionated atrial electrograms (CFAEs) in order to characterize their physiological dynamic behavior. The results were obtained considering 113 short term atrial electrograms (A-EGMs) which were annotated by three experts into four classes of fractionation according to A-EGMs signal regularity. The following measures were applied on A-EGM signals: General Correlation Dimension, Approximate Entropy, Detrended Fluctuation Analysis, Lempel-Ziv Complexity, and Katz-Sevcik, Variance and Box Counting Fractal Dimension. Assessment of disorganization was evaluated by a Kruskal Wallis statistical test. Except Detrended Fluctuation Analysis and Variance Fractal Dimension, the CFAE disorganization was found statistically significant even for low significant level alpha = 0.001. Moreover, the increasing complexity of A-EGM signals was reflected by higher values of General Correlation Dimension of order 1 and Approximate En...
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A robust, automated classification system for polysomnographic (PSG) data targeted to the newborn sleep stage identification is presented. The problem of polysomnographic signal classification is very often difficult because of artifacts... more
A robust, automated classification system for polysomnographic (PSG) data targeted to the newborn sleep stage identification is presented. The problem of polysomnographic signal classification is very often difficult because of artifacts and noise. Furthermore, for each signal, a special classification method for each particular type of segment must be mostly used. This paper proposes fully unsupervised approach using adaptive segmentation, appropriate features extraction and hierarchical clustering (Ward’s minimumvariance method is used). The mutual information concept was applied to results of hierarchical clustering. The proposed procedure was tested on real neonatal data. All sleep states were successfully separated by a combination of EEG, EMG, EOG, PNG and ECG channels.
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The ratio of three newborn’s behavioral states – wakefulness, active sleep and quiet sleep – is an important indicator of the maturity of the newborn brain in clinical practice. This paper presents study undertaken to identify possible... more
The ratio of three newborn’s behavioral states – wakefulness, active sleep and quiet sleep – is an important indicator of the maturity of the newborn brain in clinical practice. This paper presents study undertaken to identify possible improvements in processing of neonatal sleep electroencephalographic recordings. The influence of segmentation, feature extraction and feature selection on the classification accuracy was examined. The total of 97 features was extracted for each EEG channel for each segment. For selection of individual features from different channels, sequential methods were used. Naive Bayes classifier was used for final classification and performance was evaluated through cross validation. Experiments show that the combination of adaptive segmentation and selected features led to better performance. Obtained results may provide a reference for developing or enhancing neonatal sleep EEG classification algorithms. Analysis was performed on real clinical data.
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An approach for Electroencephalogram (EEG) processing is presented. Along with the the- oretical development of stochastic processing techniques, two application areas are suggested: EEG sleep recording analysis and Brain Computer... more
An approach for Electroencephalogram (EEG) processing is presented. Along with the the- oretical development of stochastic processing techniques, two application areas are suggested: EEG sleep recording analysis and Brain Computer Interface (BCI). Many methods have been already developed in the area of sleep staging, nevertheless the automatic scoring in not still so effective as the manual scoring. Our sleep scoring
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This paper describes methods of automatic analysis and classification of biological signals. Polysomnographic (PSG) recordings encompass a set of heterogeneous biological signals (eg EEG, EOG, EMG, ECG, PNG) recorded simultaneously. These... more
This paper describes methods of automatic analysis and classification of biological signals. Polysomnographic (PSG) recordings encompass a set of heterogeneous biological signals (eg EEG, EOG, EMG, ECG, PNG) recorded simultaneously. These signals, especially EEG ...
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Polysomnographic (PSG) signal processing represents a complex process consisting of several subsequent steps, namely pre-processing, segmentation, extraction of descriptive features, and classification. In this paper we focus on... more
Polysomnographic (PSG) signal processing represents a complex process consisting of several subsequent steps, namely pre-processing, segmentation, extraction of descriptive features, and classification. In this paper we focus on visualization methods that are also unseparable part of the whole process. The aim of these methods is to ease the work of medical doctors and to show trends that are not obvious when performing a manual inspection of the recorded signal. In this study, the designed methods are applied to neonatal PSG data and enable the enhancement in visual differentiation between three important behavioral states: quiet sleep (QS), active sleep (AS) and wakefulness (WK). The ratio of these states is a significant indicator of the maturity of the newborn brain in clinical practice.
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This paper explores differences between two methods for Blind Source Separation within frame of ECG de-noising. First method is Joint Approximate Diagonalization of Eigenmatrices, which is based on estimation of fourth order... more
This paper explores differences between two methods for Blind Source Separation within frame of ECG de-noising. First method is Joint Approximate Diagonalization of Eigenmatrices, which is based on estimation of fourth order cross-cummulant tensor and its diagonalization. Second one is the statistical method known as Canonical Correlation Analysis, which is based on estimation of correlation matrices between two multidimensional variables. Both methods were used within method, which combines the Blind Source Separation algorithm with decision tree. The evaluation was made on large database of 382 long-term ECG signals and the results were examined. Biggest difference was found in results of 50 Hz power line interference where the CCA algorithm completely failed. Thus main power of CCA lies in estimation of unstructured noise within ECG. JADE algorithm has larger computational complexity thus the CCA perfomend faster when estimating the components.
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Research Interests: Computer Graphics, Visual Neuroscience, Electroencephalography, Space perception, Wavelet Analysis, and 15 moreTemporal Lobe, Humans, Female, Male, Young Adult, Computer User Interface Design, Adult, Parietal Lobe, Reproducibility of Results, Electrodes, Bayes Theorem, Theta Rhythm, Beta Rhythm, Occipital Lobe, and Functional Laterality
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Polysomnography (PSG) is one of the most important noninvasive methods for studying maturation of the child brain. Sleep in infants is significantly different from sleep in adults. This paper addresses the problem of computer analysis of... more
Polysomnography (PSG) is one of the most important noninvasive methods for studying maturation of the child brain. Sleep in infants is significantly different from sleep in adults. This paper addresses the problem of computer analysis of neonatal polygraphic signals. We applied methods designed for differentiating three important neonatal behavioral states: quiet sleep, active sleep, and wakefulness. The proportion of these states is a significant indicator of the maturity of the newborn brain in clinical practice. In this study, we used data provided by the Institute for Care of Mother and Child, Prague (12 newborn infants of similar postconceptional age). The data were scored by an experienced physician to four states (wake, quiet sleep, active sleep, movement artifact). For accurate classification, it was necessary to determine the most informative features. We used a method based on power spectral density (PSD) applied to each EEG channel. We also used features derived from electrooculogram (EOG), electromyogram (EMG), ECG, and respiration [pneumogram (PNG)] signals. The most informative feature was the measure of regularity of respiration from the PNG signal. We designed an algorithm for interpreting these characteristics. This algorithm was based on Markov models. The results of automatic detection of sleep states were compared to the "sleep profiles" determined visually. We evaluated both the success rate and the true positive rate of the classification, and statistically significant agreement of the two scorings was found. Two variants, for learning and for testing, were applied, namely learning from the data of all 12 newborns and tenfold cross-validation, and learning from the data of 11 newborns and testing on the data from the 12th newborn. We utilized information obtained from several biological signals (EEG, ECG, PNG, EMG, EOG) for our final classification. We reached the final success rate of 82.5%. The true positive rate was 81.8% and the false positive rate was 6.1%. The most important step in the whole process is feature extraction and feature selection. In this process, we used visualization as an additional tool that helped us to decide which features to select. Proper selection of features may significantly influence the success rate of the classification. We made a visual comparison of the computed features with the manual scoring provided by the expert. A hidden Markov model was used for classification. The advantage of this model is that it determines the future behavior of the process by its present state. In this way, it preserves information about temporal development.
Research Interests: Engineering, Algorithms, Artificial Intelligence, Principal Component Analysis, Electroencephalography, and 24 moreFeature Selection, Clinical Practice, Fourier Analysis, Brain, Humans, Markov chains, Statistical Significance, Heart rate, Eye Movements, hidden Markov model, Feature Extraction, Electromyography, Cross Validation, Respiration, Newborn Infant, Polysomnography, Success Rate, Reproducibility of Results, Chi Square Distribution, Electrooculography, Electromyogram, Power spectral density, Sleep Stages, and Markov model
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In last decades there were many discussions about status of women in technology and engineering. Ratio of male and female university students corresponds to gender ratio in population. However when we look at individual study fields there... more
In last decades there were many discussions about status of women in technology and engineering. Ratio of male and female university students corresponds to gender ratio in population. However when we look at individual study fields there are great differences. In the paper we try to show the situation in the Czech Republic considering number of university students and gender
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ABSTRACT Complex fractionated atrial electrograms (CFAEs) represent the electrophysiologic substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify CFAEs sites is crucial for the development of AF... more
ABSTRACT Complex fractionated atrial electrograms (CFAEs) represent the electrophysiologic substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify CFAEs sites is crucial for the development of AF ablation strategies. Individual signal complexes in CFAEs reflect electrical activity of electrophysiologic substrate at given time. We developed a novel algorithm for automated search of individual signal complexes in CFAEs. This algorithm based on wavelet transform enables to describe CFAEs in a novel way and helps to classify CFAEs level of complexity (degree of fractionation). The method was tested using a representative set of 1.5s A-EGMs (n = 113) ranked by an expert into 4 categories: 1 -organized atrial activity; 2 -mild; 3 -intermediate; 4 -high degree of fractionation. Individual signal complexes were marked by an expert in every A-EGM in the dataset. This ranking was used as gold standard for comparison with the novel automatic search method. Following hit rates were achieved by performed automatic search on representative set of data: category 1: 100%, category 2: 98.2%, category 3: 92.06%, category 4: 63.89%. These results indicate that wavelet signal decomposition could carry high level of predictive information about the state of electrophysiologic substrate for AF.
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... Jiri Klema* Laboratory for System Design, Faculty of Electrical Engineering and Computer Science, Smetanova ulica 17, 2000 Maribor, Slovenia Phone: +386-62-2207455, Fax: +386-62-211178 e-mail:{matej.sprogar, kokol, milan ... [3]... more
... Jiri Klema* Laboratory for System Design, Faculty of Electrical Engineering and Computer Science, Smetanova ulica 17, 2000 Maribor, Slovenia Phone: +386-62-2207455, Fax: +386-62-211178 e-mail:{matej.sprogar, kokol, milan ... [3] Boudoulas, H., Kolibash, AJ, Baker, P., King ...