A phase-locked loop epilepsy network emulator

P. D. Watson, K. M. Horecka, R. Ratnam, N. J. Cohen

Research output: Contribution to journalArticlepeer-review


Most seizure forecasting employs statistical learning techniques that lack a representation of the network interactions that give rise to seizures. We present an epilepsy network emulator (ENE) that uses a network of interconnected phase-locked loops (PLLs) to model synchronous, circuit-level oscillations between electrocorticography (ECoG) electrodes. Using ECoG data from a canine-epilepsy model (Davis et al., 2011 [6]) and a physiological entropy measure (approximate entropy or ApEn, Pincus 1995 [21]), we demonstrate that the entropy of the emulator phases increases dramatically during ictal periods across all ECoG recording sites and across all animals in the sample. Further, this increase precedes the observable voltage spikes that characterize seizure activity in the ECoG data. These results suggest that the ENE is sensitive to phase-domain information in the neural circuits measured by ECoG and that an increase in the entropy of this measure coincides with increasing likelihood of seizure activity. Understanding this unpredictable phase-domain electrical activity present in ECoG recordings may provide a target for seizure detection and feedback control.

Original languageEnglish (US)
Pages (from-to)1245-1249
Number of pages5
StatePublished - Jan 15 2016


  • Approximate entropy
  • Electrocorticography
  • Epilepsy emulation
  • Neural network
  • Phase locked loop

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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