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Nonlinear Directed Interactions Between HRV and EEG Activity in Children With TLE.

Karin Schiecke, Britta Pester, Diana Piper

    IEEE Transactions on Bio-Medical Engineering
    |June 16, 2016
    PubMed
    Summary

    Convergent cross mapping (CCM) reveals directed interactions between heart rate variability (HRV) and EEG activity in children with temporal lobe epilepsy (TLE). These nonlinear interactions change during seizures, offering insights into the epileptic network and autonomic nervous system (ANS).

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Clinical Neurology

    Background:

    • Epileptic seizures significantly impact the autonomic nervous system (ANS), with heart rate variability (HRV) serving as a key indicator of ANS alterations.
    • Previous research identified linear, undirected interactions between HRV and electroencephalogram (EEG) activity before, during, and after seizures.
    • Investigating directed nonlinear interactions is crucial for a deeper understanding of seizure onset development.

    Purpose of the Study:

    • To explore directed nonlinear interactions between HRV and EEG activity in children with temporal lobe epilepsy (TLE).
    • To apply Convergent Cross Mapping (CCM) and Autoregressive (AR)-based partial directed coherence (PDC) to analyze these complex interactions.
    • To elucidate the dynamic interplay between the epileptic network and the ANS during seizure activity.

    Main Methods:

    • Convergent Cross Mapping (CCM) was employed to analyze nonlinear, directed interactions between time series derived from HRV and specific EEG components in pediatric TLE patients.
    • Time-varying multivariate Autoregressive (AR) models were used for partial directed coherence (PDC) estimation.
    • Simulated data were used to assess the influence of estimation parameters and time-varying behavior on CCM.
    • Bootstrapping and surrogate data approaches were utilized for statistical validation of interactions.

    Main Results:

    • CCM successfully revealed time-varying directed interactions between HRV and delta-related EEG activity in pediatric TLE.
    • Less pronounced interactions were observed between HRV and alpha-related EEG activity, with EEG components predominantly driving HRV.
    • The interaction patterns and directionality significantly shifted with seizure onset.
    • AR-based PDC estimation proved inadequate for analyzing the clinical data, whereas CCM provided valuable time-course and frequency-selective insights.

    Conclusions:

    • CCM is a powerful tool for uncovering complex, nonlinear, and directed interactions between physiological signals like HRV and EEG in neurological disorders.
    • The findings highlight significant alterations in the interplay between the epileptic network and the ANS during seizures in children with TLE.
    • CCM offers a more effective approach than AR-based PDC for understanding the dynamic temporal and frequency-selective characteristics of these interactions.