Nonlinear autoregressive analysis of the 3/s ictal electroencephalogram: implications for underlying dynamics

Nicholas D. Schiff, Jonathan D. Victor, Annemarie Canel

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

In a previous study, nonlinear autoregressive (NLAR) models applied to ictal electroencephalogram (EEG) recordings in six patients revealed nonlinear signal interactions that correlated with seizure type and clinical diagnosis. Here we interpret these models from a theoretical viewpoint. Extended models with multiple nonlinear terms are employed to demonstrate the independence of nonlinear dynamical interactions identified in the 'NLAR fingerprint' of patients with 3/s seizure discharges. Analysis of the role of periodicity in the EEG signal reveals that the fingerprints reflect the dynamics not only of the periodic discharge itself, but also of the fluctuations of each cycle about an average waveform. A stability analysis is used to make qualitative inferences concerning the network properties of the ictal generators. Finally, the NLAR fingerprint is analyzed in the context of Volterra-Weiner theory.

Original languageEnglish
Pages (from-to)527-532
Number of pages6
JournalBiological Cybernetics
Volume72
Issue number6
DOIs
StatePublished - May 1995
Externally publishedYes

Fingerprint

Dive into the research topics of 'Nonlinear autoregressive analysis of the 3/s ictal electroencephalogram: implications for underlying dynamics'. Together they form a unique fingerprint.

Cite this