Time, frequency and information domain analysis of short-term heart rate variability before and after focal and generalized seizures in epileptic children

Pernice, Riccardo; Faes, Luca; Kotiuchyi, Ivan; Stivala, Salvatore; Busacca, Alessandro; Попов, Антон; Kharytonov, Volodymyr

doi:10.1088/1361-6579/ab16a3

Cited by 21 publications

(28 citation statements)

References 62 publications

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“…While several studies have investigated interictal autonomic nervous system (ANS) changes in epilepsy (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21) and PNES (22)(23)(24)(25)(26), no information is yet available about putative differences in ANS activity alterations induced by pPNES as compared to PNES/ES. In this context, a sympathetic overdrive higher than that observed during PNES was described in temporal lobe seizures (24,27).…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability Parameters During Psychogenic Non-epileptic Seizures: Comparison Between Patients With Pure PNES and Comorbid Epilepsy

Romigi

Rizzo

Izzi³

et al. 2020

Front. Neurol.

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Section: Introductionmentioning

confidence: 99%

Heart Rate Variability Parameters During Psychogenic Non-epileptic Seizures: Comparison Between Patients With Pure PNES and Comorbid Epilepsy

Romigi

Rizzo

Izzi³

et al. 2020

Front. Neurol.

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“…These results are in line with previous findings [47] evidencing that, in response to an action which evokes localized occipital brain reactions like the eyes closure, information storage is informative also when computed at the scalp level, likely because of its univariate nature, while information transfer is blurred to uniform values due to the adverse effects of volume conduction on connectivity measures. To highlight the importance of a distinction between different type of seizures, we remark that differences have been evidenced also in relation to autonomic nervous regulation, e.g., probed by measures (including information-theoretic ones) of heart rate variability computed before and after focal and generalized seizures [40].…”

Section: Discussionmentioning

confidence: 99%

“…The EEG time series were then normalized to zero mean and unit variance before calculating information theoretic measures. More details on data acquisition and pre-processing are given in previous studies [40,41].…”

Section: Experimental Protocol Data Preprocessing and Statistical Anmentioning

confidence: 99%

“…Antiepileptic medications were administered in therapeutic dosages to the patients according to their diagnosis, i.e., (i) children with focal seizures: carbamazepine, lamotrigine, lacosamide, topiramate, valproic acid; (ii) children with generalized seizures: valproic acid, lamotrigine, topiramate, and vigabatrin [ 40 ].…”

Section: Application To Eeg Signals In Epileptic Childrenmentioning

confidence: 99%

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A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks

et al. 2020

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This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on simulated EEGs obtained mixing source signals generated under different coupling conditions, showing its ability to retrieve source information dynamics from the scalp signals. Then, it was applied to investigate scalp and source brain connectivity in a group of children manifesting episodes of focal and generalized epilepsy; the analysis was performed on EEG signals lasting 5 s, collected in two consecutive windows preceding and one window following each ictal episode. Our results show that generalized seizures are associated with a significant decrease from pre-ictal to post-ictal periods of the information stored in the signals and of the information transferred among them, reflecting reduced self-predictability and causal connectivity at the level of both scalp and source brain dynamics. On the contrary, in the case of focal seizures the scalp EEG activity was not discriminated across conditions by any information measure, while source analysis revealed a tendency of the measures of information transfer to increase just before seizures and to decrease just after seizures. These results suggest that focal epileptic seizures are associated with a reorganization of the topology of EEG brain networks which is only visible analyzing connectivity among the brain sources. Our findings emphasize the importance of EEG modeling approaches able to deal with the adverse effects of volume conduction on brain connectivity analysis, and their potential relevance to the development of strategies for prediction and clinical treatment of epilepsy.

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“…We chose signal entropy as a potential signal marker of interest, since entropy has been shown to increase prior to certain state changes in neural systems before 14 . Related to epilepsy entropy was previously used for example in EEG 15 and ECG 16 data. Entropy was calculated for each segment as H = −p log p, with log denoting the logarithm to base 2 and p being the probability density obtained by binning the data into n bins.…”

Section: Data Recording We Recruited Patients Admitted To the Long-tmentioning

confidence: 99%

Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures

Vieluf

Reinsberger

Atrache

et al. 2020

Sci Rep

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A better understanding of the early detection of seizures is highly desirable as identification of an impending seizure may afford improved treatments, such as antiepileptic drug chronotherapy, or timely warning to patients. While epileptic seizures are known to often manifest also with autonomic nervous system (ANS) changes, it is not clear whether ANS markers, if recorded from a wearable device, are also informative about an impending seizure with statistically significant sensitivity and specificity. Using statistical testing with seizure surrogate data and a unique dataset of continuously recorded multi-day wristband data including electrodermal activity (EDA), temperature (TEMP) and heart rate (HR) from 66 people with epilepsy (9.9 ± 5.8 years; 27 females; 161 seizures) we investigated differences between inter-and preictal periods in terms of mean, variance, and entropy of these signals. We found that signal mean and variance do not differentiate between inter-and preictal periods in a statistically meaningful way. EDA signal entropy was found to be increased prior to seizures in a small subset of patients. Findings may provide novel insights into the pathophysiology of epileptic seizures with respect to ANS function, and, while further validation and investigation of potential causes of the observed changes are needed, indicate that epilepsy-related state changes may be detectable using peripheral wearable devices. Detection of such changes with wearable devices may be more feasible for everyday monitoring than utilizing an electroencephalogram. The current inability to assess when a seizure is most likely to occur constitutes a major burden for people with epilepsy (PWE) 1. From a clinical perspective, this inability precludes the development of better treatments, such as antiepileptic drug chronotherapy adapted to personalized risk profiles, or timely, closed-loop intervention strategies to acutely avert impending seizures 2. Hence, a better understanding of the informative biomarkers underlying the transition to seizures is needed. Most research devoted to the study of seizure onset mechanisms and prior warning signals has traditionally focused on electroencephalogram (EEG). Continuous EEG, however, is impractical for monitoring over extended periods of time, especially when used in the ambulatory setting 3. Wearable devices might offer a promising alternative, as these afford easy-to-use, close monitoring of autonomic nervous system (ANS) function without being invasive or restraining to PWE 4. Alterations of ANS activity are known to occur frequently within multiple domains, such as electrodermal, thermal and cardiac subsystems, in relation to seizures, and show specific patterns across these parameters 5-7. However, further research on these subsystems of the ANS and whether they may afford statistically meaningful identification of preictal periods in terms of sensitivity and specificity is needed.

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Time, frequency and information domain analysis of short-term heart rate variability before and after focal and generalized seizures in epileptic children

Cited by 21 publications

References 62 publications

Heart Rate Variability Parameters During Psychogenic Non-epileptic Seizures: Comparison Between Patients With Pure PNES and Comorbid Epilepsy

Heart Rate Variability Parameters During Psychogenic Non-epileptic Seizures: Comparison Between Patients With Pure PNES and Comorbid Epilepsy

A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks

Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures

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