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Biomedical subjects

V Krajca

Publications and source records attributed to V Krajca.

8 recordsLinked to original sources
PubMed2003

EEG photic driving in workers exposed to mercury vapors.

OBJECTIVE: To assess the potential of EEG photic driving (PD) as an indicator of an early neurotoxic effect of long-term, low-level exposure to mercury vapors. SUBJECTS AND METHODS: Twenty-four chloralkali workers exposed to mercury vapors; twenty-four age- and gender-matched control subjects. Level of exposure was determined by urinary mercury excreted both spontaneously and after administration of a chelating agent, sodium 2,3-dimercapto-1-propane sulfonate. A computerized method for quantitative evaluation of PD was developed. Five parameters describing PD were compared. RESULTS: The number of stimulation frequencies eliciting PD was higher in the exposed group, with a median of 17 frequencies, as compared to 10 frequencies in the control group (P < 0.001). The maximum value of PD was higher in the exposed group, with a median of 24.6 z-units as compared to 9.4 in the control group (P < 0.001). The median of the stimulation frequency with maximum PD was shifted from 15 Hz in the control group to 20 Hz in the exposed group (P < 0.01). The median of the sum of PD and the median of the index of PD were significantly higher in the exposed than in the control group (P < 0.001). The increased PD was particularly prominent at high stimulation frequencies in the beta range. There was no significant association between the measures of PD and the measures of exposure. CONCLUSIONS: In comparison with a control group, significantly increased photic driving was observed in a group of workers exposed to mercury vapors. The issue of whether or nor the intergroup differences in PD are mercury related, could not be determined on the basis of our results. Should the enhanced PD be caused by mercury, then this electrophysiological phenomenon might be regarded as a marker of the CNS hyperexcitability due to an early neurotoxic effect of mercury, the clinical expression of which is erethism.

Adult↗
PubMed1999

Extraction of principal components from biosignals by neural net.

This contribution gives the information on a useful application of principal component analysis (PCA) in the field of electroencephalogram (EEG) and laser-Doppler signal processing. The principal components are estimated by a neural network (NN) approach.

Algorithms↗
PubMed1995

Structure optimization of neural networks with A* -algorithm application in EEG pattern analysis.

The A* - Algorithm for heuristic search is applied to construct a Neural Network structure (NS) that optimally fits the structure of data to be learned. In this way, the user of Neural Networks (NN) is able to avoid the empirical testing of different structures. The method given here is applied to the recognition of different patterns derived from the EEG of an epileptic patient.

Algorithms↗
PubMed1995

Application of optimized pattern recognition units in EEG analysis: common optimization of preprocessing and weights of neural networks as well as structure optimization.

The main goal of this study is to demonstrate the possibility of training the Neural Network (multilayer perceptron) classifier and preprocessing units simultaneously, i.e., that properties of preprocessing are chosen automatically during the training phase. In the first realization step, adaptive recursive estimation of the power within a frequency band was used as a preprocessing unit. To improve the efficiency of special units, the power and momentary frequency estimation was replaced by methods that are based on adaptive Hilbert transformers. The strategy was developed to obtain optimized recognition units that can be efficiently integrated into strategies for monitoring the cerebral status of neonates. Therefore, applications (e.g., in neonatal EEG pattern recognition) will be shown. Additionally, a method of minimizing the error function was used, where this minimization is based on optimizing the network structure. The results of structure optimization in the field of EEG pattern recognition in epileptic patients can be demonstrated.

Algorithms↗
PubMed1991

Use of discrete Hilbert transformation for automatic spike mapping: a methodological investigation.

On the basis of discrete Hilbert transform (DHT) realised by fast Fourier transform (FFT), a new strategy for automatic spike mapping is introduced. The further computation of the EEG time series after DHT results in the time series of the momentary power and the momentary frequency. Both are used for the solution of the main requirements of automatic spike mapping. The spike-mapping concept introduced meets the requirements of efficient automatic spike detection and also has an insensitivity with regard to EMG interference and transient signal components, a frequent cause of false positive detections. Additionally, there are advantages if the momentary power of the spike is mapped instead of the spike potential. The use of momentary power makes a combination of power spectral mapping and spike-mapping strategies possible.

Algorithms↗
PubMed1984

Automatic classification of EEG segments and extraction of representative ones by dynamic clusters method.

Use of the dynamic clusters method for automatic extraction of compressed information about recorded EEG signal is presented. The computer first divides the record into quasi-stationary segments by means of adaptive segmentation. Second, the extracted segments are classified by a method of dynamic clusters into homogeneous classes. One part of the used clustering algorithm permits to specify and draw the most typical class members, which may represent the whole studied EEG signal and may be used as input for the further phase of the automatic EEG analysis, i.e. for the classification of the whole EEG records. The above procedure was applied to a 75 sec long EEG record of anaesthetized cat intoxicated by CO.

Computers↗
PubMedDate not supplied

Automatic identification of significant graphoelements in multichannel EEG recordings by adaptive segmentation and fuzzy clustering.

A new approach to visual evaluation of long-term EEG recordings is proposed. The method is based on multichannel adaptive segmentation, subsequent feature extraction, automatic classification of the acquired segments by fuzzy cluster analysis (fuzzy c-means algorithm), and on the distinguishing of thus identified EEG segments by colour directly in the EEG record. The black and white variant of the described automatic system is presented. The method was evaluated by applying it to simulated artificial data and to real EEG recordings; some of the illustrative results are shown. In addition, the performance of this system is evaluated and the first experience with its application to routine EEG recordings is discussed.

Algorithms↗