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220 Daphnia magna as a model for quantifying chaos in cardiac arrhythmia
  1. Andrew M. Whiteoak1,
  2. Peter E. Penson2
  1. 1Pharmacy and Biomolecular Sciences, Liverpool John Moores University
  2. 2School of Pharmacy and Bimolecular Sciences, Liverpool John Moores University


Introduction Daphnia magna are an established model in ecology for the investigation of toxins in freshwater systems, as well as an emerging model in medical science. Daphnia have a myogenic heart, exhibiting responses comparable to that of the human heart to a range of established therapeutics, and displaying varying arrhythmias on exposure to pro-arrhythmic agents. Given the multitude of mathematical methods put forward to predict arrhythmia, it is surprising as yet none are in clinical use. This study aims to rectify this issue.

Methods D. magna cardiac action was captured on HD film for periods of 24 s (120+ heart contractions) both prior to and following chemical induction of cardiac arrhythmia. A novel semi-automatic process gave heart area values over the full 1440 frames per film. Along with time domain data, this gave parameters for heart rate and cardiac output after parabolic peak interpolation. Data were analysed in linear terms, including ellipse fitting1 and standard deviation of successive differences;2 and in non-linear terms including complex correlation,3 multi-scale ratio analysis,4 median stepping increment5 and finite time growth.6

Results Results demonstrate that non-linear analysis methods are superior to linear methods in differentiating cardiac arrhythmias from both one another and from normal rhythm. While most published methods do not differentiate arrhythmic heart conditions with significance, finite time growth, by contrast, may offer some headway toward a robust method of quantifying cardiac arrhythmia.

Implications The Daphnia filmatographic model presents an opportunity to examine heart action in vivo; offering highly accessible means of assessing both current and developing models for the prediction of arrhythmias.


  1. Mohebbi M, Ghassemian H. Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal. Comput Methods Programs Biomed 2012;105(1):40–9.

  2. Galland BC, Taylor BJ, B, et al. Heart rate variability and cardiac reflexes in small for gestational age infants. J App Physiology 2006;100(3):933–9.

  3. . Karmakar CK, Khandoker AH, et al. Complex Correlation Measure: a novel descriptor for Poincaré plot. BioMed Eng OnLine 2009;8(17).

  4. . Huo C, Huang X, et al. A multi-scale feedback ratio analysis of heartbeat interval series in healthy vs. cardiac patients. Med Eng Phys 2014;36(12):1693–1698.

  5. . Gong Y, Lu Y, et al. Predict Defibrillation Outcome Using Stepping Increment of Poincare Plot for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest. BioMed Res Int 2015;(493472):7.

  6. . Wessel N, Voss A, et al. Nonlinear analysis of complex phenomena in cardiological data. Herzschr Elektrophys 2000;11:159–173

  • arrhythmia
  • animal model
  • Poincare

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