Heart rate variability in patients with refractory epilepsy: The influence of generalized convulsive seizures

Intra-Individual Relationship between Heart Rate Variability and the Underlying Heart Rate in Children and Adolescents

Background/Objective: The relationship between heart rate and heart rate variability (HRV) indices has been repeatedly studied in adults but limited data are available on the relationship in paediatric populations. Methods: Continuous 12-lead electrocardiograms were recorded in 1016 healthy children and adolescents (534 females) aged 4 to 19 years during postural manoeuvres with rapid changes between 10-min positions of supine → sitting → standing → supine → standing → sitting → supine. In each position, the averaged RR interval was measured together with four HRV indices, namely the SDNN, RMSSD, quasi-normalised high-frequency components (qnHF), and the proportions of low- and high-frequency components (LF/HF). In each subject, the slope of the linear regression between the repeated HRV measurements and the corresponding RR interval averages was calculated. Results: The intra-subject regression slopes, including their confidence intervals, were related to the age and sex of the subjects. The SDNN/RR, RMSSD/RR, and qnHF/RR slopes were significantly steeper (p < 0.001) and the (LF/HF)/RR slopes were significantly shallower (p < 0.001) in younger children compared to older children and adolescents. Conclusions: The study suggests that sympathetic and vagal influences on heart rate are present in both younger and older children. With advancing age, the sympatho-vagal balance gradually develops and allows the vagal control to suppress the sympathetic drive towards higher heart rates seen in younger age children.

Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review

The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events.

Wearable device assessments of antiseizure medication effects on diurnal patterns of electrodermal activity, heart rate, and heart rate variability

Patient-generated health data provide a great opportunity for more detailed ambulatory monitoring and more personalized treatments in many diseases. In epilepsy, robust diagnostics applicable to the ambulatory setting are needed as diagnosis and treatment decisions in current clinical practice are primarily reliant on patient self-reports, which are often inaccurate. Recent work using wearable devices has focused on methods to detect and forecast epileptic seizures. Whether wearable device signals may also contain information about the effect of antiseizure medications (ASMs), which may ultimately help to better monitor their efficacy, has not been evaluated yet. Here we systematically investigated the effect of ASMs on different data modalities (electrodermal activity, EDA, heart rate, HR, and heart rate variability, HRV) simultaneously recorded by a wearable device in 48 patients with epilepsy over several days in the epilepsy long-term monitoring unit at a tertiary hospital. All signals exhibited characteristic diurnal variations. HRV, but not HR or EDA-based metrics, were reduced by ASMs. By assessing multiple signals related to the autonomic nervous system simultaneously, our results provide novel insights into the effects of ASMs on the sympathetic and parasympathetic interplay in the setting of epilepsy and indicate the potential of easy-to-wear wearable devices for monitoring ASM action. Future work using longer data may investigate these metrics on multidien cycles and their utility for detecting seizures, assessing seizure risk, or informing treatment interventions.