Measures and Metrics of Biological Signals

The concept of biological signals is becoming broader. Some of the challenges are: searching for inner and structural characteristics; selecting appropriate modeling to enhance perceived properties in the signals; extracting the representative components, identifying their mathematical correspondents; and performing necessary transformations in order to obtain form for subtle analysis, comparisons, derived recognition, and classification. There is that unique moment when we correspond the adequate mathematical structures to the observed phenomena. It allows application of various mathematical constructs, transformations and reconstructions. Finally, comparisons and classifications of the newly observed phenomena often lead to enrichment of the existing models with some additional structurality. For a specialized context the modeling takes place in a suitable set of mathematical representations of the same kind, a set of models M, where the mentioned transformations take place. They are used for determination of structures M, where mathematical finalization processes are preformed. Normalized representations of the initial content are measured in order to determine the key invariants (characterizing characteristics). Then, comparisons are preformed for specialized or targeted purposes. The process converges to the measures and distance measurements in the space M. Thus, we are dealing with measure and metric spaces, gaining opportunities that have not been initially available. Obviously, the different aspects in the research or diagnostics will demand specific spaces. In our practice we faced a large variety of problems in analysis of biological signals with very rich palette of measures and metrics. Even when a unique phenomena are observed for slightly different aspects of their characteristics, the corresponding measurements differ, or are refinements of the initial structures. Certain criteria need to be fulfilled. Namely, characterization and semantic stability. The small changes in the structures have to induce the small changes in measures and metrics. We offer a collection of the models that we have been involved in, together with the problems we met and their solutions, with representative visualizations.

[1]  Luiz A. Baccalá,et al.  Partial Directed Coherence Asymptotics for VAR Processes of Infinite Order , 2008 .

[2]  Luiz A. Baccalá,et al.  Partial Directed Coherence , 2014 .

[3]  G. A. Miller,et al.  Improved Estimation of Human Cortical Activity and Connectivity with the Multimodal Integration of Neuroelectric and Hemodynamic Data , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[4]  N. Japundzic-Zigon Physiological mechanisms in regulation of blood pressure fast frequency variations. , 1998, Clinical and experimental hypertension.

[5]  Srdjan Kesić,et al.  Ouabain modulation of snail Br neuron bursting activity after the exposure to 10 mT static magnetic field revealed by Higuchi fractal dimension. , 2014, General physiology and biophysics.

[6]  N. Japundzic-Zigon,et al.  EFFECTS OF NONPEPTIDE V1a AND V2 ANTAGONISTS ON BLOOD PRESSURE FAST OSCILLATIONS IN CONSCIOUS RATS , 2001, Clinical and experimental hypertension.

[7]  Fred Hasselman,et al.  When the blind curve is finite: dimension estimation and model inference based on empirical waveforms , 2013, Front. Physiol..

[8]  J. L. Roux An Introduction to the Kalman Filter , 2003 .

[9]  Michael Eichler,et al.  Abstract Journal of Neuroscience Methods xxx (2005) xxx–xxx Testing for directed influences among neural signals using partial directed coherence , 2005 .

[10]  Obrad Kasum Enhancing microscopic imaging for better object and structural detection, insight and classification , 2014 .

[11]  Aneta Brzezicka,et al.  Information Transfer During a Transitive Reasoning Task , 2010, Brain Topography.

[12]  J. Geweke,et al.  Measures of Conditional Linear Dependence and Feedback between Time Series , 1984 .

[13]  Evor L. Hines,et al.  Classification and feature extraction strategies for multi channel multi trial BCI data , 2007 .

[14]  N. Japundzic-Zigon,et al.  Effects of nonpeptide and selective V1 and V2 antagonists on blood pressure short-term variability in spontaneously hypertensive rats. , 2004, Journal of pharmacological sciences.

[15]  C. Granger Testing for causality: a personal viewpoint , 1980 .

[16]  Maciej Kamiński,et al.  Transmission of information during Continuous Attention Test. , 2008, Acta neurobiologiae experimentalis.

[17]  Xue Wang,et al.  Granger Causality between Multiple Interdependent Neurobiological Time Series: Blockwise versus Pairwise Methods , 2007, Int. J. Neural Syst..

[18]  Aleksandar Jovanovic,et al.  Brain Computer Interface - some technical remarks , 2007 .

[19]  Endre Pap,et al.  Handbook of measure theory , 2002 .

[20]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[21]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[22]  Srdjan Kesic,et al.  Application of Higuchi's fractal dimension from basic to clinical neurophysiology: A review , 2016, Comput. Methods Programs Biomed..

[23]  Jerome R. Busemeyer,et al.  Quantum Models of Cognition and Decision , 2012 .

[24]  J. Geweke,et al.  Measurement of Linear Dependence and Feedback between Multiple Time Series , 1982 .

[25]  W. Klonowski,et al.  Forensics of Features in the Spectra of Biological Signals , 2010 .

[26]  F. Stephan,et al.  Set theory , 2018, Mathematical Statistics with Applications in R.

[27]  S. Bressler,et al.  Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Katarzyna J. Blinowska,et al.  A new method of the description of the information flow in the brain structures , 1991, Biological Cybernetics.

[29]  T. Hughes,et al.  Signals and systems , 2006, Genome Biology.

[30]  Hans Hermes,et al.  Introduction to mathematical logic , 1973, Universitext.

[31]  Jianbo Gao,et al.  Fractal analyses: statistical and methodological innovations and best practices , 2013, Front. Physiol..

[32]  Maciej Kaminski,et al.  Transmission of Brain Activity During Cognitive Task , 2010, Brain Topography.

[33]  Luiz A. Baccalá,et al.  Information theoretic interpretation of frequency domain connectivity measures , 2010, Biological Cybernetics.

[34]  Wlodzislaw Duch,et al.  Detection of Structural Features in Biological Signals , 2010, J. Signal Process. Syst..

[35]  Silva Hecimovic,et al.  Loss of Cathepsin B and L Leads to Lysosomal Dysfunction, NPC-Like Cholesterol Sequestration and Accumulation of the Key Alzheimer's Proteins , 2016, PloS one.

[36]  R. Solovay A model of set-theory in which every set of reals is Lebesgue measurable* , 1970 .

[37]  Mary Attenborough,et al.  Mathematics for electrical engineering and computing , 2003 .

[38]  Wlodzislaw Duch,et al.  Some Computational Aspects of the Brain Computer Interfaces Based on Inner Music , 2009, Comput. Intell. Neurosci..

[39]  S. Bressler,et al.  Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data , 2006, Journal of Neuroscience Methods.

[40]  P. Molenaar A Manifesto on Psychology as Idiographic Science: Bringing the Person Back Into Scientific Psychology, This Time Forever , 2004 .

[41]  Gary Bradski,et al.  Computer Vision Face Tracking For Use in a Perceptual User Interface , 1998 .

[42]  Frank H. Guenther,et al.  Brain-computer interfaces for speech communication , 2010, Speech Commun..

[43]  F. Babiloni,et al.  The Estimation of Cortical Activity for Brain-Computer Interface: Applications in a Domotic Context , 2007, Comput. Intell. Neurosci..

[44]  Clive W. J. Granger,et al.  Time Series Modelling and Interpretation , 1976 .

[45]  Katarzyna J. Blinowska,et al.  Review of the methods of determination of directed connectivity from multichannel data , 2011, Medical & Biological Engineering & Computing.

[46]  L. Rakić,et al.  Differential effects of amphetamine and phencyclidine on the expression of growth-associated protein GAP-43 , 2001, Neuroscience Research.

[47]  T. Takedac,et al.  Magnetoencephalographic Study of Auditory Feature Analysis Associated with Visually Based Prediction , 2009 .

[48]  Clive W. J. Granger,et al.  Time series modeling and interpretation , 2001 .

[49]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[50]  A. Perović,et al.  Automatic Recognition of Features in Spectrograms Based on some Image Analysis Methods , 2013, Acta Polytechnica Hungarica.

[51]  L. Baccalá,et al.  Overcoming the limitations of correlation analysis for many simultaneously processed neural structures. , 2001, Progress in brain research.

[52]  C. Granger Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[53]  Mingzhou Ding,et al.  Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance , 2001, Biological Cybernetics.

[54]  Aleksandar Jovanovic,et al.  Brain connectivity extended and expanded , 2015 .

[55]  Koichi Sameshima,et al.  Using partial directed coherence to describe neuronal ensemble interactions , 1999, Journal of Neuroscience Methods.

[56]  Katarzyna J. Blinowska,et al.  Methods for localization of time-frequency specific activity and estimation of information transfer in brain. , 2008 .