Obtaining the Heart Rate Information from the Speckle Images by Fractal Analysis Method

Abstract

Heart rate is the main data that shows if the heart is working properly. Therefore, obtaining the heart rate information has a vital importance. There are some methods to measure the heart rate, but the most commonly used one is the Electrocardiography (ECG). However, this method is expensive and non-portable. Therewithal, optical studies have recently been conducted to measure heart rate. Being non-invasive, inexpensive, and safe are the advantages of optical measurements. Laser speckle contrast imaging is an effective and simple technique for imaging heterogeneous environments such as human and animal tissues. By laser speckle contrast analysis, heart rate can be obtained easily. It is the standard technique, but fractal analysis method is also very convenient way to study speckle images because speckle pattern is quite appropriate for studying fractality due to its granular structure. In this paper, we present fractal analysis method for obtaining heart rate information from speckle images. The results of this method for the various in-vivo and in-vitro data were compared with the reference model results of speckle contrast analysis method and it is observed that the proposed analysis method has provided sufficient results.

Keywords

• Fractal analysis,box counting,speckle,heart rate

References

1. 1. Cohn A.E. 1925. Physiological Ontogeny: A chicken embryos. V. on the rate of the heart beat during the development of chicken embryos, J. Exp. Med., 42(3): 291–297.
2. 2. Writing Group Members, et al. 2006. Heart disease and stroke statistics—2006 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee, Circulation, 113.6, e85-e151.
3. 3. Malik, Marek, et al. 1996. Heart rate variability: Standards of measurement, physiological interpretation, and clinical use, European Heart Journal, 17(3): 354-381.
4. 4. Pahnvar, A.J. 2016. Estimation of near subcutaneous blood microcirculation related blood flow using laser speckle contrast imaging, Ege University Graduate School of Natural and Applied Sciences Department of Electrical and Electronics Engineering.
5. 5. Laguna, P., et al. 1990. New algorithm for QT interval analysis in 24-hour Holter ECG: performance and applications, Medical and Biological Engineering and Computing, 28(1): 67-73.
6. 6. Gottdiener, John S., et al. 2004. American Society of Echocardiography recommendations for use of echocardiography in clinical trials: A report from the American society of echocardiography's guidelines and standards committee and the task force on echocardiography in clinical trials, Journal of the American Society of Echocardiography, 17(10): 1086-1119.
7. 7. Boas, David A., and Dunn, A.K. 2010. Laser speckle contrast imaging in biomedical optics, Journal of Biomedical Optics, 15(1): 011109.
8. 8. Dainty, J. Christopher, ed. 2013. Laser speckle and related phenomena, Springer Science & Business Media, 9.

9. 9. Nemati, M., et al. 2016. Fractality of pulsatile flow in speckle images, Journal of Applied Physics, 119(17): 174902. 10. Zhang, X., et. al., A low-cost and smartphone-based laser speckle contrast imager for blood flow, BIBE 2018; International Conference on Biological Information and Biomedical Engineering, Shanghai, China, 2018.
10. 11. Escobar, C.P.V., New laser speckle methods for in vivo blood flow imaging and monitoring, Universitat Politecnica de Catalunya ICFO, 2014. 12. Briers, J. D. 2001. Laser Doppler, speckle and related techniques for blood perfusion mapping and imaging, Physiological Measurement, 22(4): 35-66.
11. 13. Boeing, G. 2016. Visual analysis of nonlinear dynamical systems: Chaos, fractals, self-similarity and the limits of prediction, Systems, 4(4), 37. 14. Falconer, K., Fractal geometry: mathematical foundations and applications, Wiley, 2013.
12. 15. Tamas, V., Fractal growth phenomena, World Scientific, 1992.
13. 16. Tan, C.O., et al. 2009. Fractal properties of human heart period variability: physiological and methodological implications, The Journal of Physiology, 2009, 587.15, 3929-3941.
14. 17. AC03515164, A., ed., Fractals: complex geometry, patterns, and scaling in nature and society, World Scientific, 1997.
15. 18. Gültepe, M.D., and Tek, Z. 2018. Investigation of phase transitions in nematic liquid crystals by fractional calculation, Celal Bayar University Journal of Science, 14(4): 373-377.
16. 19. Lopes, R., and Nacim B. 2009. Fractal and multifractal analysis: a review, Medical Image Analysis, 13(4): 634-649.
17. 20. Mandelbrot, B.B., The fractal geometry of nature, New York: WH Freeman, 1983, Vol. 173.
18. 21. Iannaccone, P. M., and Khokha, M., Fractal geometry in biological systems: an analytical approach, CRC Press, 1996.
19. 22. Li, J., Qian, D., and Caixin, S. 2009. An improved box-counting method for image fractal dimension estimation, Pattern Recognition, 42(11): 2460-2469.
20. 23. So, G.K., Hye-Rim, S., and Gang-Gyoo, J. 2017. Enhancement of the box-counting algorithm for fractal dimension estimation, Pattern Recognition Letters, 98: 53-58.