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Digital signal processing methods for impedance microfluidic cytometry

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Abstract

Impedance microfluidic cytometry is a non-invasive, label-free technology that can characterize the dielectric properties of single particles (beads/cells) at high speed. In this paper we show how digital signal processing methods are applied to the impedance signals for noise removal and signal recovery in an impedance microfluidic cytometry. Two methods are used; correlation to identify typical signals from a particle and for a noisier environment, an adaptive filter is used to remove noise. The benefits of adaptive filtering are demonstrated quantitatively from the correlation coefficient and signal-to-noise ratio. Finally, the adaptive filtering method is compared to the Savitzky–Golay filtering method.

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Acknowledgments

This work was partly funded by the University of Southampton, Life Science Initiative (LSI) and Philips. The authors would like to thank Mr. Rong Zhang for valuable discussions.

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Authors and Affiliations

  1. Nanoscale Systems Integration Group, School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK

    Tao Sun, Nicolas G. Green & Hywel Morgan

  2. Philips Research Laboratory, Cross Oak Lane, Redhill, Surrey, RH1 5HA, UK

    Cees van Berkel

Authors
  1. Tao Sun
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  2. Cees van Berkel
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  3. Nicolas G. Green
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  4. Hywel Morgan
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Correspondence to Hywel Morgan.

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Sun, T., van Berkel, C., Green, N.G. et al. Digital signal processing methods for impedance microfluidic cytometry. Microfluid Nanofluid 6, 179–187 (2009). https://doi.org/10.1007/s10404-008-0315-3

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  • DOI: https://doi.org/10.1007/s10404-008-0315-3

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