Abstract
A number of technical problems arise in the acquisition and processing of real biomedical data (biosignals — such as EEG, EMG, ECG, some evoked potentials, and so on). In general, physicians would rather buy standard equipment for their clinical examinations. However, by so doing they are totally dependent on the market supply, because when installed the equipment is usually hard-wired and not changeable by the user. Therefore problems may arise when they wish to update their equipment and experiment with some new non-standard method. In such cases scientists would rather construct their own equipment. In practice, they often buy some minicomputer or personal computer with a graphical display, equip it with a plotter and A/D converters and connect it to some EEG, EMG or ECG apparatus. Then they write software for their own methods of examination themselves. By operating in this manner, writing their particular routines takes a great deal of time. The work may be difficult and the results are non-standard, and usually non-compatible with other systems, such that they are hardly usable elsewhere.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bastiaans, M. J., 1980, Gabor’s expansion of a signal into Gaussian elementary signals, Proc. IEEE 68: 538.
Dvorřák, I., and Šiška, J., 1986, On some problem encountered in calculating the correlation dimension of EEG, Phys. Letters A 118(2): 63.
Dvorřák, I., Šiška, J., Wackerman, J., Hrudovg, L., and Dostalek, C., 1986, Evidence for interpretation of EEG as a deterministic chaotic process with a low dimension, Activitas Neurose Superior 28(3): 228.
French, A. S., and Butz, E. G., 1973, Measuring the Wiener kernels of a non-linear system using the Fast Fourier Transform algorithm, Int. J. Contr., 17: 529.
French, A. S., and Butz, E. G., 1974, The use of Walsh Functions in the Wiener analysis of nonlinear systems, IEEE Trans. Comput., C-23(3): 225.
French, A. S., 1976, Practical nonlinear system analysis by Wiener kernel estimation in the frequency domain, Biol. Cybern., 24: 111.
Gabor, D., 1946, Theory of communication, J. IEE: 429.
Herman, P., 1978, Nonlinear Analysis of Electroneurological Preparats, MMF UK, Prague (in Czech).
Kadri, F. L., 1972, A method of determining the crosscorrelation functions for a class of non-linear systems, Int. J. Contr., 15: 779.
Koblasz, A. J., 1978, Nonlinearities of the human ERG reflected by Wiener kernels, Biol. Cybern., 31(4): 187.
Koblasz, A., Rae, J. L., Correia, M. J., and Ni, M.-D., 1980, Wiener kernels and frequency response functions for the human retina, IEEE Trans. Biomed. Eng., BME-27(2): 68.
Krausz, H., 1975, Identification of nonlinear systems using random impulse train inputs, Biol. Cybern., 19: 217.
Larkin, R. M., Klein, S., Odgen, T. E., and Tender, D. H., 1979, Nonlinear kernels of the human ERG, Biol. Cybern., 35(3): 145.
Lee, Y. W., and Schetzen, M., 1965, Measurement of the Wiener kernels of a non-linear system by cross-correlation, Int. J. Contr., 2: 237.
Marmarelis, P. Z., 1972, Nonlinear identification of bioneural system through white-noise stimulation, in: “Proc. 13th Joint Automatic Control Conference”, Stanford: 117.
Marmarelis, P. Z., and Naka, K. I., 1972, White noise analysis of a neuron chain: an application of the Wiener theory, Science 175: 1276.
Marmarelis, P. Z., and McCann, G. D., 1973, Development and application of white-noise modeling techniques for studies of insect visual nervous system, Kybernetik 12: 74.
Marmarelis, P. Z., and Naka, K. I., 1973, Non-linear analysis and synthesis of receptive field responses in the catfish retina, J. Neurophysiol., 36: 605.
Marmarelis, P. Z., and Naka, K. I., 1974, Identification of multi-input biological systems, IEEE Trans. Biomed. Eng., BME-2l(2): 88.
Marmarelis, P. Z., and Marmarelis, V. Z., 1978, “Analysis of Physiological Systems”, Plenum, New York.
Moore, G., Herman, P., Krekule, I., and Bureg, J., 1977, Application of the nonlinear Wiener theory of the analysis of evoked activity of an epileptic focus in the motor cortex in rats, in: “3rd Conference of Biomedical Engineering”, Brno.
Rajbman, N. S., and Terechin, A. T., 1965, Dispersion methods of random functions and their use for study of nonlinear control plants, Avtomatika i Telemechanika 26: 500 (in Russian).
Rajbman, N. S., and Han, O. F., 1967, Dispersion methods of identification of multidimensional nonlinear control plants, Avtomatika i Telemechanika, 28(5):5 (in Russian).
Rajbman, N. S., 1981, “Dispersion Analysis”, Nauka, Moscow (in Russian).
Seelen, W.von, and Hoffmann, K.-P., 1976, Analysis of neuronal networks in the visual system of the cat using statistical signals, Biol. Cybern., 22:7.
Trimble, J., and Phillips, G., 1978, Nonlinear analysis of the human visual evoked response, Biol. Cybern., 30(1):55.
Wickesberg, R. E., and Geisler, C. G., 1984, Artifacts in Wiener kernels estimated using Gaussian white noise, IEEE Trans. Biomed. Eng., BME-31: 454.
Wiener, N., 1958, “Nonlinear Problems in Random Theory”, Wiley, New York.
Yasui, S., Davis, W., and Naka, K. I., 1979, Spatio-temporal receptive field measurement of retinal neurons by random pattern stimulation and cross-correlation, IEEE Trans. Biomed. Eng., BME-26: 263.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1988 Plenum Press, New York
About this paper
Cite this paper
Heřman, P. (1988). R@-System — The Software System for Real Biomedical Data Acquisition and Processing with Regard to Clinic and Research. In: Carson, E.R., Kneppo, P., Krekule, I. (eds) Advances in Biomedical Measurement. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1025-9_23
Download citation
DOI: https://doi.org/10.1007/978-1-4613-1025-9_23
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4612-8298-3
Online ISBN: 978-1-4613-1025-9
eBook Packages: Springer Book Archive