Dynamic cross-spectral analysis of biological signals by means of bivariate ARMA processes with time-dependent coefficients

Dynamic cross-spectral analysis of biological signals were conducted using bivariate autoregressive moving average (ARMA) processes with time-dependent coefficients. Owing to the recursive structure of all algorithms involved, a continuous calculation of cross-spectral density is possible and allows...

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Bibliographic Details
Published in:Medical & biological engineering & computing 1995-07, Vol.33 (4), p.605-610
Main Authors: SCHACK, B, GRIESZBACH, G, ARNOLD, M, BOLTEN, J
Format: Article
Language:English
Subjects:
Algorithms
Biological and medical sciences
Computerized, statistical medical data processing and models in biomedicine
Electroencephalography
General aspects. Methods
Humans
Medical sciences
Microcirculation
Retinal Vessels - physiology
Signal Processing, Computer-Assisted
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Summary:Dynamic cross-spectral analysis of biological signals were conducted using bivariate autoregressive moving average (ARMA) processes with time-dependent coefficients. Owing to the recursive structure of all algorithms involved, a continuous calculation of cross-spectral density is possible and allows a detailed time coherence analysis. The ability of the estimations of adaptation to structural changes in the signals is demonstrated. The algorithm was tested in two main applications: EEG analysis and microcirculation in the retina.
ISSN:0140-0118
1741-0444
DOI:10.1007/BF02522521