Prolonged Video-EEG and Heart Rate Variability can Elucidate Autonomic Dysregulation in Infantile Apneic Seizures

Maruyama, Shinsuke; Jain, Puneet; Parbhoo, Kaajal; Go, Cristina; Shibata, Takashi; Otsubo, Hiroshi

doi:10.1016/j.pediatrneurol.2021.11.007

Cited by 1 publication

(1 citation statement)

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“…Two initial works, published in 2004 and 2005, analyzed HR in children with different epilepsy types, assessing that ictal tachycardia is more commonly associated with convulsive seizures [57,58]. Several studies (N = 6) denoted a correlation between seizures and sympathetic dominance, considering HRV metrics computed in both the time and the frequency domain [49,51,53,[59][60][61]. A single study suggested that electrical seizures do not involve any HRV modifications [62], while Okanari and colleagues showed that children with post-ictal EEG suppression are associated with stronger sympathetic dominance during the pre-ictal phase [63].…”

Section: Pediatric Populationmentioning

confidence: 99%

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

Mason,

Scarabello,

Taruffi

et al. 2024

JCM

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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.

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