Automatic parametrization of somatosensory evoked potentials with chirp modeling

Eero Vayrynen, Kai Noponen, Ashwati Vipin, X. Y. Thow, Hasan Al-Nashash, Jukka Kortelainen, Angelo All

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


In this paper, an approach using polynomial phase chirp signals to model somatosensory evoked potentials (SEPs) is proposed. SEP waveforms are assumed as impulses undergoing group velocity dispersion while propagating along a multipath neural connection. Mathematical analysis of pulse dispersion resulting in chirp signals is performed. An automatic parameterization of SEPs is proposed using chirp models. A Particle Swarm Optimization algorithm is used to optimize the model parameters. Features describing the latencies and amplitudes of SEPs are automatically derived. A rat model is then used to evaluate the automatic parameterization of SEPs in two experimental cases, i.e., anesthesia level and spinal cord injury (SCI). Experimental results show that chirp-based model parameters and the derived SEP features are significant in describing both anesthesia level and SCI changes. The proposed automatic optimization based approach for extracting chirp parameters offers potential for detailed SEP analysis in future studies. The method implementation in Matlab technical computing language is provided online.

Original languageEnglish (US)
Article number7399773
Pages (from-to)981-992
Number of pages12
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number9
StatePublished - Sep 2016


  • Anesthesia
  • biological system modeling
  • parameter estimation
  • particle swarm optimization (PSO)
  • spinal cord injury (SPI)

ASJC Scopus subject areas

  • Internal Medicine
  • General Neuroscience
  • Biomedical Engineering


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