306
M. Thanaj et al.
analysis of the blood flux signals has begun to shed new light on the flexibility of the
system in response to standard haemodynamic perturbations and in human cohorts
at increasing risk and severity of cardiovascular and metabolic disease. Nonlinear
complexity methods have been used to quantify the regularity of the blood flux signal
by evaluating the presence of repeated patterns, providing complexity variants at
single and across multiple spatial and temporal scales. Further, the new approach of
attractor reconstruction analysis offers quantitative measures of the microvascular
system in phase space and a visual representation in the shape and variability of
the signal producing a two-dimensional attractor with features such as density and
symmetry. Nonlinear analysis thus provides a better characterisation of the flexibility
of the system in a range of pathophysiological conditions.
In conclusion, these mathematical approaches are able to identify changes in
microvascular function and have utility in understanding the fundamental mechanistic contributors to microvascular (dys)function. To what extent they can be used
to discriminate between (patho)physiological states and in the understanding of the
effectiveness of interventions for reversing established vasculopathy and perfusion
impairments has yet to be determined. They have yet to be used to inform treatment
regimens or to predict clinical outcomes. With machine learning techniques these
novel approaches may, in future, support a more effective and mechanism-based
classification of tissue perfusion, providing a use in clinical assessment.
References
1. M. Aboy, R. Hornero, D. Abasolo, D. Alvarez, Interpretation of the Lempel-Ziv complexity
measure in the context of biomedical signal analysis. IEEE Trans. Biomed. Eng. 53(11), 2282–
2288 (2006)
2. A.M. Albano, X.T. Tigno, P.E. Rapp, Time series analysis, or the quest for qualitative measures
of time dependent behavior. Philipp. Sci. Lett. 1(1), 18–31 (2008)
3. J. Allen, K. Howell, Microvascular imaging: techniques and opportunities for clinical
physiological measurements. Physiol. Meas. 35(7), R91–R141 (2014)
4. P.J. Aston, M. Nandi, M.I. Christie, Y.H. Huang (eds.), Comparison of attractor reconstruction
and HRV methods for analysing blood pressure data. Comput. Cardiol. 2014 (2014)
5. P.J. Aston, M.I. Christie, Y.H. Huang, M. Nandi, Beyond HRV: attractor reconstruction using
the entire cardiovascular waveform data for novel feature extraction. Physiol. Meas. 39(2),
024001– (2018)
6. K. Balasubramanian, N. Nagaraj, Aging and cardiovascular complexity: effect of the length of
RR tachograms. Peerj 4 (2016)
7. M.A. Basarab, N.S. Konnova, D.A. Basarab, D.D. Matsievskiy (eds.), Digital signal processing
of the Doppler blood flow meter using the methods of nonlinear dynamics, in 2017 Progress
in Electromagnetics Research Symposium—Spring (PIERS), 22–25 May 2017
8. A. Bernjak, A. Stefanovska, Importance of wavelet analysis in laser Doppler flowmetry time
series. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1, 4064–4067 (2007)
9. A. Bernjak, A. Stefanovska, P.V.E. McClintock, P.J. Owen-Lynch, P.B.M. Clarkson, SAMP.
Coherence between fluctuations in blood flow and oxygen saturation. Fluct. Noise Lett. 11(1),
1240013 (2012)
M. Thanaj et al.
analysis of the blood flux signals has begun to shed new light on the flexibility of the
system in response to standard haemodynamic perturbations and in human cohorts
at increasing risk and severity of cardiovascular and metabolic disease. Nonlinear
complexity methods have been used to quantify the regularity of the blood flux signal
by evaluating the presence of repeated patterns, providing complexity variants at
single and across multiple spatial and temporal scales. Further, the new approach of
attractor reconstruction analysis offers quantitative measures of the microvascular
system in phase space and a visual representation in the shape and variability of
the signal producing a two-dimensional attractor with features such as density and
symmetry. Nonlinear analysis thus provides a better characterisation of the flexibility
of the system in a range of pathophysiological conditions.
In conclusion, these mathematical approaches are able to identify changes in
microvascular function and have utility in understanding the fundamental mechanistic contributors to microvascular (dys)function. To what extent they can be used
to discriminate between (patho)physiological states and in the understanding of the
effectiveness of interventions for reversing established vasculopathy and perfusion
impairments has yet to be determined. They have yet to be used to inform treatment
regimens or to predict clinical outcomes. With machine learning techniques these
novel approaches may, in future, support a more effective and mechanism-based
classification of tissue perfusion, providing a use in clinical assessment.
References
1. M. Aboy, R. Hornero, D. Abasolo, D. Alvarez, Interpretation of the Lempel-Ziv complexity
measure in the context of biomedical signal analysis. IEEE Trans. Biomed. Eng. 53(11), 2282–
2288 (2006)
2. A.M. Albano, X.T. Tigno, P.E. Rapp, Time series analysis, or the quest for qualitative measures
of time dependent behavior. Philipp. Sci. Lett. 1(1), 18–31 (2008)
3. J. Allen, K. Howell, Microvascular imaging: techniques and opportunities for clinical
physiological measurements. Physiol. Meas. 35(7), R91–R141 (2014)
4. P.J. Aston, M. Nandi, M.I. Christie, Y.H. Huang (eds.), Comparison of attractor reconstruction
and HRV methods for analysing blood pressure data. Comput. Cardiol. 2014 (2014)
5. P.J. Aston, M.I. Christie, Y.H. Huang, M. Nandi, Beyond HRV: attractor reconstruction using
the entire cardiovascular waveform data for novel feature extraction. Physiol. Meas. 39(2),
024001– (2018)
6. K. Balasubramanian, N. Nagaraj, Aging and cardiovascular complexity: effect of the length of
RR tachograms. Peerj 4 (2016)
7. M.A. Basarab, N.S. Konnova, D.A. Basarab, D.D. Matsievskiy (eds.), Digital signal processing
of the Doppler blood flow meter using the methods of nonlinear dynamics, in 2017 Progress
in Electromagnetics Research Symposium—Spring (PIERS), 22–25 May 2017
8. A. Bernjak, A. Stefanovska, Importance of wavelet analysis in laser Doppler flowmetry time
series. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1, 4064–4067 (2007)
9. A. Bernjak, A. Stefanovska, P.V.E. McClintock, P.J. Owen-Lynch, P.B.M. Clarkson, SAMP.
Coherence between fluctuations in blood flow and oxygen saturation. Fluct. Noise Lett. 11(1),
1240013 (2012)