Measuring Peripheral Signal Complexity Values by Applying Non-linear Method to Medical Laser Contrast Images
DOI:
https://doi.org/10.61841/rwb2tp65Keywords:
ALaser contrast images, Image processing,, Information theory,, Microcirculation., ,Entropy,Abstract
The physiological signals are considered as a sensitive measure for describing human state. The evaluation of such signals could be accomplished by monitoring peripheral blood flow in the skin. Laser speckle contrast imaging (LSCI) is an optical-based imaging device produces high quality images of microvascular blood flow. In this article, a research study is presented to measure signal complexity values of leg microvascular blood flow into two age healthy groups. Microvascular blood flow of leg skin was acquired using LSCI. The research sample consisted of 18 healthy individuals, divided into two groups according to the age: young group (20 to 30 years old, n=10) and aged group (50 to 70 years old, n=10). Signal complexity values of microvascular blood flow were computed by applying nonlinear algorithm to LSCI data of leg. The use of nonlinear methods to compute leg signal complexity values by processing LSCI data revealed higher entropy values obtained from young group than the ones obtained from aged group. However, the differences on signal complexity values between young and aged groups were not significant (p=0.65). Furthermore, applying nonlinear methods to LSCI data of leg could be used to estimate the deterioration of blood flow in peripheral vessels. Further studies with more subjects are needed to confirm the results presented in this paper. In addition, a comparative study with another body part may give new insight on the peripheral vessels dysfunction associated with aging.
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1. Allen, J., and Kevin H., "Microvascular imaging: techniques and opportunities for clinical physiological measurements." Physiological measurement 35(7), pp. R91–R141, (2014). https://doi.org/10.1088/0967-3334/35/7/R91
2. Bi, Renzhe, et al. "Optical methods for blood perfusion measurement—theoretical comparison among four different modalities." JOSA A 32.5 (2015): 860-866. https://doi.org/10.1364/JOSAA.32.000860
3. Richards, Lisa M., et al. "Low-cost laser speckle contrast imaging of blood flow using a webcam." Biomedical optics express 4.10 (2013): 2269-2283. https://doi.org/10.1364/BOE.4.002269
4. Briers, J. David. "Laser speckle contrast imaging for measuring blood flow." (2007).
5. Stern, M. D. "In vivo evaluation of microcirculation by coherent light scattering." Nature 254.5495 (1975): 56-58. https://doi.org/10.1038/254056a0
6. Richman, Joshua S., and J. Randall Moorman. "Physiological time-series analysis using approximate entropy and sample entropy." American Journal of Physiology-Heart and Circulatory Physiology 278.6 (2000): H2039-H2049. https://doi.org/10.1152/ajpheart.2000.278.6.H2039
7. Costa, M., A. L. Goldberger, and C-K. Peng. "Multiscale entropy to distinguish physiologic and synthetic RR time series." Computers in cardiology. IEEE, 2002.
8. Humeau-Heurtier, Anne, et al. "Multiscale entropy study of medical laser speckle contrast images." IEEE Transactions on Biomedical Engineering 60.3 (2012): 872-879.
https://doi.org/10.1109/TBME.2012.2208642
9. Khalil, Adil, et al. "Laser speckle contrast imaging: age-related changes in microvascular blood flow and correlation with pulse-wave velocity in healthy subjects." Journal of biomedical optics 20.5 (2014): 051010. https://doi.org/10.1117/1.JBO.20.5.051010
10. Humeau-Heurtier, Anne, et al. "Multiscale compression entropy of microvascular blood flowsignals: Comparison of results from laser speckle contrastand laser Doppler flowmetry data in healthy subjects." Entropy 16.11 (2014): 5777-5795. https://doi.org/10.3390/e16115777
11. Khalil, Adil, et al. "Aging effect on microcirculation: A multiscale entropy approach on laser speckle contrast images." Medical physics 43.7 (2016): 4008-4016. https://doi.org/10.1118/1.4953189
12. Humeau, Anne, et al. "Multiscale analysis of microvascular blood flow: A multiscale entropy study of laser Doppler flowmetry time series." IEEE transactions on biomedical engineering 58.10 (2011): 2970-2973. https://doi.org/10.1118/1.3512796
13. Humeau, Anne, et al. "Multiscale entropy of laser Doppler flowmetry signals in healthy human subjects." Medical physics 37.12 (2010): 6142-6146. https://doi.org/10.1118/1.3512796
14. Goldberger, Ary L., C-K. Peng, and Lewis A. Lipsitz. "What is physiologic complexity and how does it change with aging and disease?." Neurobiology of aging 23.1 (2002): 23-26.
https://doi.org/10.1016/S0197-4580(01)00266-4
15. Lin, Pei-Feng, et al. "Correlations between the signal complexity of cerebral and cardiac electrical activity: a multiscale entropy analysis." PloS one 9.2 (2014). https://doi.org/10.1371/journal.pone.0087798
16. Tikhonova, Irina V., Arina V. Tankanag, and Nikolay K. Chemeris. "Time–amplitude analysis of skin blood flow oscillations during the post-occlusive reactive hyperemia in human." Microvascular research 80.1 (2010): 58-64. https://doi.org/10.1016/j.mvr.2010.03.010
17. Yvonne-Tee, Get Bee, et al. "Method optimization on the use of postocclusive hyperemia model to assess microvascular function." Clinical hemorheology and microcirculation 38.2 (2008): 119-133.
18. Harris, Norman R., and Rolando E. Rumbaut. "Age-related responses of the microcirculation to ischemia-reperfusion and inflammation." Pathophysiology 8.1 (2001): 1-10.
https://doi.org/10.1016/S0928-4680(01)00064-5
19. Kenney, W. Larry, and Thayne A. Munce. "Invited review: aging and human temperature regulation." Journal of applied physiology 95.6 (2003): 2598-2603.
https://doi.org/10.1152/japplphysiol.00202.2003
20. El‐Domyati, M., et al. "Intrinsic aging vs. photoaging: a comparative histopathological, immunohistochemical, and ultrastructural study of skin." Experimental dermatology 11.5 (2002): 398-
405. https://doi.org/10.1034/j.1600-0625.2002.110502.x
21. Waller, Jeanette M., and Howard I. Maibach. "Age and skin structure and function, a quantitative approach (I): blood flow, pH, thickness, and ultrasound echogenicity." Skin research and technology
11.4 (2005): 221-235. https://doi.org/10.1111/j.0909-725X.2005.00151.x
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