26 April 2018 Analysis of cerebral vessels dynamics using experimental data with missed segments
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Abstract
Physiological signals often contain various bad segments that occur due to artifacts, failures of the recording equipment or varying experimental conditions. The related experimental data need to be preprocessed to avoid such parts of recordings. In the case of few bad segments, they can simply be removed from the signal and its analysis is further performed. However, when there are many extracted segments, the internal structure of the analyzed physiological process may be destroyed, and it is unclear whether such signal can be used in diagnostic-related studies. In this paper we address this problem for the case of cerebral vessels dynamics. We perform analysis of simulated data in order to reveal general features of quantifying scaling features of complex signals with distinct correlation properties and show that the effects of data loss are significantly different for experimental data with long-range correlations and anti-correlations. We conclude that the cerebral vessels dynamics is significantly less sensitive to missed data fragments as compared with signals with anti-correlated statistics.
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O. N. Pavlova, A. S. Abdurashitov, M. V. Ulanova, G. M. Shihalov, O. V. Semyachkina-Glushkovskaya, A. N. Pavlov, "Analysis of cerebral vessels dynamics using experimental data with missed segments", Proc. SPIE 10717, Saratov Fall Meeting 2017: Laser Physics and Photonics XVIII; and Computational Biophysics and Analysis of Biomedical Data IV, 1071725 (26 April 2018); doi: 10.1117/12.2309627; https://doi.org/10.1117/12.2309627
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