Ultradian Rhythms in Accelerometric and Autonomic Data Vary Based on Seizure Occurrence in Paediatric Epilepsy Patients

Authors

Solveig Vieluf
Sarah Cantley
Vaishnav Krishnan
Tobias Loddenkemper

Publication Date

1-1-2024

Journal

Brain Communications

DOI

10.1093/braincomms/fcae034

PMID

38454964

PMCID

PMC10919479

PubMedCentral® Posted Date

2-12-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

cyclic patterns, autonomic activity, accelerometry, epilepsy, paediatric patients

Abstract

Ultradian rhythms are physiological oscillations that resonate with period lengths shorter than 24 hours. This study examined the expression of ultradian rhythms in patients with epilepsy, a disease defined by an enduring seizure risk that may vary cyclically. Using a wearable device, we recorded heart rate, body temperature, electrodermal activity and limb accelerometry in patients admitted to the paediatric epilepsy monitoring unit. In our case–control design, we included recordings from 29 patients with tonic–clonic seizures and 29 non-seizing controls. We spectrally decomposed each signal to identify cycle lengths of interest and compared average spectral power- and period-related markers between groups. Additionally, we related seizure occurrence to the phase of ultradian rhythm in patients with recorded seizures. We observed prominent 2- and 4-hour-long ultradian rhythms of accelerometry, as well as 4-hour-long oscillations in heart rate. Patients with seizures displayed a higher peak power in the 2-hour accelerometry rhythm (U = 287, P = 0.038) and a period-lengthened 4-hour heart rate rhythm (U = 291.5, P = 0.037). Those that seized also displayed greater mean rhythmic electrodermal activity (U = 261; P = 0.013). Most seizures occurred during the falling-to-trough quarter phase of accelerometric rhythms (13 out of 27, χ2 = 8.41, P = 0.038). Fluctuations in seizure risk or the occurrence of seizures may interrelate with ultradian rhythms of movement and autonomic function. Longitudinal assessments of ultradian patterns in larger patient samples may enable us to understand how such rhythms may improve the temporal precision of seizure forecasting models.

Comments

Associated Data