1. Iolov, A, Ditlevsen S and Longtin, A (2014) Fokker-Planck and Fortet equation-based parameter estimation for a leaky integrate-and-fire model with sinusoidal and stochastic forcing. J. Math. Neurosci. 4, 4.
  2. Seely, AJE, Bravi, A, Herry C, Green G, Longtin A, et al. (2014) Added prognostic accuracy and predictive model of heart and respiratory rate variability to forecast extubation outcomes. Crit. Care 18, R65.
  3. Bravi, A, Green, G, Herry, C, Wright, HE, Longtin, A, Kenny, GP and Seely, AJE (2013) Do physiological and pathological stresses produce different changes in heart rate variability? Frontiers Physiol. 4, 197.
  4. Bravi, A, Green, G, Longtin, A and Seely, AJE (2012) Monitoring and identification of sepsis development through a composite measure of heart rate variability. PLoS One 7(9), e45666. (pdf)
  5. Bravi, A, Longtin, A and Seely, AJE (2011) Review and classification of variability analysis techniques for clinical applications. BMC Biomed. Engin. Online 10: 90 (Oct.10)
  6. Engbert, R., Longtin, A. and Kliegl, R. (2004) Complexity of eye movements in reading: A theoretical model. Intern. J. Bifurc. Chaos 14, 493-503. (pdf)
  7. Capurro, A., Longtin, A., Bagarinao, E., Sato, S., Macadar, O. and Pakdaman, K. (2001) Variability of the electric organ discharge interval duration in resting Gymnotus carapo. Biol. Cybern. 84, 309-321. (pdf)
  8. Racicot, D. and Longtin, A. (1997) Interspike interval attractors from chaotically driven neuron models. Physica D 104, 184-204. (pdf)
  9. Longtin, A. (1997) Nonlinear prediction of biosignals. Japan Soc. of Med. Electron. and Biol. Engin. BME 11(1), 11-17. (invited review)
  10. Longtin, A. and Racicot, D.M. (1997) Spike train patterning and forecastability. Biosystems 40, 111-118. (pdf)
  11. Longtin, A. and D.M. Racicot (1997) Assessment of linear and nonlinear correlations between neural firing events. In: Nonlinear Dynamics and Time Series: Building a Bridge between the Natural and Statistical Sciences, eds. C.D. Cutler and D.T. Kaplan, Fields Institute Communications Vol.11, 223-239.
  12. Theiler, J., Galdrikian, B., Longtin, A., Eubank, S. and Farmer, J.D. (1992) Testing for nonlinearity in time series: the method of surrogate data. Physica D 58: 77-94. (Reprinted in: Coping with Chaos. Analysis of Chaotic Data and the Exploitation of Chaotic Systems, E. Ott, T. Sauer and J.A. Yorke, eds., Wiley Series in Nonlinear Science, pp.124-141 (1994)). (pdf)
  13. Theiler, J., Galdrikian, B., Longtin, A., Eubank, S. and Farmer, J.D. (1992) Detecting nonlinear structure in time series. Proceedings, First Experimental Chaos conference, Arlington, Virginia (World Scientific, Singapore) p.47-53.
  14. Theiler, J., Galdrikian, B., Longtin, A., Eubank, S. and Farmer, J.D. (1991) Using surrogate data to detect nonlinearity in time series. In Nonlinear Prediction and Modeling, M. Casdagli and S. Eubank, eds. (Addison-Wesley, Redwood City, Ca).