In contrast to AR-based estimation of PDC CCM was able to reveal time-courses and frequency-selective views of nonlinear interactions for the further understanding of complex interactions between the epileptic network and the ANS in children with TLE.
Time-variant coherence analysis between the heart rate variability (HRV) and the channel-related envelopes of adaptively selected EEG components was used as an indicator for the occurrence of (correlative) couplings between the central autonomic network (CAN) and the epileptic network before, during and after epileptic seizures. Two groups of patients were investigated, a group with left and a group with right hemispheric temporal lobe epilepsy. The individual EEG components were extracted by a signal-adaptive approach, the multivariate empirical mode decomposition, and the envelopes of each resulting intrinsic mode function (IMF) were computed by using Hilbert transform. Two IMFs, whose envelopes were strongly correlated with the HRV's low-frequency oscillation (HRV-LF; ≈0.1 Hz) before and after the seizure were identified. The frequency ranges of these IMFs correspond to the EEG delta-band. The timevariant coherence was statistically quantified and tensor decomposition of the time-frequency coherence maps was applied to explore the topography-timefrequency characteristics of the coherence analysis. Results allow the hypothesis 4 Both authors contributed equally to the publication, and are considered as first author. that couplings between the CAN, which controls the cardiovascularcardiorespiratory system, and the 'epileptic neural network' exist. Additionally, our results confirm the hypothesis of a right hemispheric lateralization of sympathetic cardiac control of the HRV-LF.Epileptic seizure activity can cause alterations of the autonomic nervous system (ANS) in different ways and heart rate variability (HRV) is most frequently used to investigate both longterm and short-term alterations of the ANS in response to the type of epilepsy and to the evolution of the epileptic seizure [1,2]. HRV is an indicator for neuronal influences on the cardiac pacemaker and is one of the important functions of the ANS. Therefore, seizure-related HRV reactions may provide more information on the organization of the ANS [3] and the mechanisms supporting ANS changes.The main divisions of the ANS are the parasympathetic and the sympathetic nervous systems, which typically function in opposition to each other. The coordination of 'autonomic functions and the ongoing behavioral needs of the organism through the activities of the somatomotor, endocrine and autonomic systems ' [4] can be attributed to a central autonomic network (CAN) [5], which involves several interconnected areas distributed throughout brainstem and forebrain. According to the ANS's structure some of these CAN areas regulate parasympathetic output whereas others regulate sympathetic output, e.g. in order to control visceral function and homeostasis as well as to adapt to internal or external challenges [6].From the point of view of network physiology [7], the temporal lobe epilepsy (TLE)related 'epileptic network' and the CAN can be considered as coupled networks. Taking this into account, couplings between EEG and HRV signals before, during and after an epilepti...
The major aim of our study is to demonstrate that a concerted combination of time-variant, frequency-selective, linear and nonlinear analysis approaches can be beneficially used for the analysis of heart rate variability (HRV) in epileptic patients to reveal premonitory information regarding an imminent seizure and to provide more information on the mechanisms leading to changes of the autonomic nervous system. The quest is to demonstrate that the combined approach gains new insights into specific short-term patterns in HRV during preictal, ictal, and postictal periods in epileptic children. The continuous Morlet-wavelet transform was used to explore the time-frequency characteristics of the HRV using spectrogram, phase-locking, band-power and quadratic phase coupling analyses. These results are completed by time-variant characteristics derived from a signal-adaptive approach. Advanced empirical mode decomposition was utilized to separate out certain HRV components, in particular blood-pressure-related Mayer waves (≈0.1 Hz) and respiratory sinus arrhythmia (≈0.3 Hz). Their time-variant nonlinear predictability was analyzed using local estimations of the largest Lyapunov exponent (point prediction error). Approximately 80-100 s before the seizure onset timing and coordination of both HRV components can be observed. A higher degree of synchronization is found and with it a higher predictability of the HRV. All investigated linear and nonlinear analyses contribute with a specific importance to these results.
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