Characterization Speckle Effect on Measurement of Blood Flow Using Sensor Based on Self-Mixing Interferometry

Ahmad Zaki Dzulfikar, Agus Rubiyanto, Endarko Endarko

Abstract


The applications of Self-Mixing Interferometry (SMI) have been popular in many fields, including biomedical signals. The self-mixing effect occurs from the coherent back-coupling of the reflected or scattered lights from a target surface. The reflected lights will be detected by a photodiode which has been integrated in one device with the laser. That's why the SMI sensor is quite practical, affordable and simple. However, SMI has the serious problem with the presence of speckle effect in measured signal. The speckle effect produced by the human tissue is called “biospeckles.” The biospeckles observed from the skin tissues contain information about the blood flow in dermal capillarities, heartbeat, and others. These biospeckle patterns cause random modulations that will be detected as random amplitude and spectrum by photodiode. In this paper we present a technique to characterize speckle effect on measurement of blood flow in fingertip using sensor based on Self-Mixing Interferometry (SMI). We used a laser diode 785 nm as a light source and a constant current of 70 mA as a current source which is irradiated on the skin tissue in the fingertip. Then, the backscattered light reenters the laser cavity and it will be detected by photodiode. The SMI signal with speckle effect will be processed by Continuous Wavelet Transform for reconstruction and detection fringe. Signal processing results show that the number of detected speckle fringes depends largely on determining the number of wavelet waves and the scale used. The fringe pattern resulting from the reconstruction of the signal can be used to determine the frequency of speckles due to object movement. The average speckle frequency of fingertip is 0,5-0,7 Hz

Keywords


self-mixing interferometry; speckle effect; continuous wavelet transform

Full Text:

PDF

References


A. Arasanz, F. J. Azcona, S. Royo, A. Jha, and J. Pladellorens, “A new method for the acquisition of arterial pulse wave using self-mixing interferometry,” Opt. Laser Technol., vol. 63, pp. 98–104, Nov. 2014.

S. K. Ozdemir, S. Takamiya, S. Ito, S. Shinohara, and H. Yoshida, “Self-mixing laser speckle velocimeter for blood flow measurement,” IEEE Trans. Instrum. Meas., vol. 49, no. 5, pp. 1029–1035, 2000.

Y. Wei et al., “Double-path acquisition of pulse wave transit time and heartbeat using self-mixing interferometry,” Opt. Commun., vol. 393, pp. 178–184, Jun. 2017.

A. Jha, F. J. Azcona, C. Yañez, and S. Royo, “Extraction of vibration parameters from optical feedback interferometry signals using wavelets,” Appl. Opt., vol. 54, no. 34, p. 10106, Dec. 2015.

U. Zabit, O. D. Bernal, and T. Bosch, “Self-mixing laser sensor for large displacements: signal recovery in the presence of speckle,” IEEE Sens. J., vol. 13, no. 2, pp. 824–831, Feb. 2013.

S. K. Ozdemir, I. Ohno, and S. Shinohara, “A comparative study for the assessment on blood flow measurement using self-mixing laser speckle interferometer,” IEEE Trans. Instrum. Meas., vol. 57, no. 2, pp. 355–363, 2008.

J. Hast, “Self-mixing interferometry and its applications in noninvasive pulse detection,” University of Oulu, 2003.

A. Jha, S. Royo, F. Azcona, and C. Yanez, “Extracting vibrational parameters from the time-frequency map of a self mixing signal: An approach based on wavelet analysis,” in IEEE SENSORS 2014 Proceedings, 2014, pp. 1881–1884.

S. Donati, G. Martini, and T. Tambosso, “Speckle pattern errors in self-mixing interferometry,” IEEE J. Quantum Electron., vol. 49, no. 9, pp. 798–806, Sep. 2013




DOI: http://dx.doi.org/10.12962/j23546026.y2019i1.5116

Refbacks

  • There are currently no refbacks.


View my Stat: Click Here

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.