Multiday cycles of heart rate are associated with seizure likelihood: An observational cohort study

Abstract

Background: Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology, and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly, and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological systems can provide new insight into the causes of multiday cycles. The brain-heart link has previously been considered in epilepsy research, with potential implications for seizure forecasting, therapy, and mortality (i.e., sudden unexpected death in epilepsy).

Methods: We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study sought to examine multiday cycles of heart rate and seizures in adults with diagnosed uncontrolled epilepsy (N=31) and healthy adult controls (N=15) using wearable smartwatches and mobile seizure diaries over at least four months (M=12.0, SD=5.9; control M=10.6, SD=6.4). Cycles in heart rate were detected using a continuous wavelet transform. Relationships between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase.

Findings: Heart rate cycles were found in all 46 participants (people with epilepsy and healthy controls), with circadian (N=46), about-weekly (N=25) and about-monthly (N=13) rhythms being the most prevalent. Of the participants with epilepsy, 19 people had at least 20 reported seizures, and 10 of these had seizures significantly phase locked to their multiday heart rate cycles.

Interpretation: Heart rate cycles showed similarities to multiday epileptic rhythms and may be comodulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting, and may also have implications for cardiovascular disease. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans.

Funding: This research received funding from the Australian Government National Health and Medical Research Council (investigator grant 1178220), the Australian Government BioMedTech Horizons program, and the Epilepsy Foundation of America's 'My Seizure Gauge' grant.

Keywords: Epilepsy; heart rate; seizure cycles; seizure forecasting; wearables.

Fig. 1

Distribution of heart rate cycles. (a) Cycle strength (expressed as the normalised wavelet power, y-axis) for different periods (x-axis, logarithmic scale) averaged across the cohort. Note that wavelet power was normalised between 0 and 1 (by subtracting the minimum and dividing by the range) for each participant to facilitate visualization. (b) Raster plot showing cycle strength (colour bar) for each individual (y-axis) at different periods (logarithmic scale). (c, d) Number of people (y-axis) with significant cycles at different periods up to 40 days (x-axis) for men (N=10) and women (N=21), respectively. Note that the x-axis (up to 40 days) is a subset of the x-axis in panels a and b (up to 167 days) as indicated by the grey arrows and black dotted lines.

Fig. 2

Examples of multiday heart rate cycles. Data are shown for two different participants, P21 (a-c) and P30 (d-f). (a, d): Heart rate (y-axis) smoothed with a 2-day moving average filter shows multiday cycles. Insets (blue) show circadian rhythms of heart rate. (b, e): A graphical representation of the bandpass filtered heart rate signals for different cycles (corresponding respectively to spectrum peaks in panels (c, f). Note that the signal amplitudes for different cycles (coloured traces) have been normalised to the same range. (d, g): Wavelet power spectra for different scales (x-axis). Significant cycle periods (peaks) are labelled with coloured dots.

Fig. 3

Distribution of heart rate cycles in people without epilepsy. (a): Cycle strength (expressed as the normalised wavelet power, y-axis) for different periods (x-axis, logarithmic scale) averaged across the cohort. (b, c): Number of people (y-axis) with significant cycles at different periods up to 40 days (x-axis) for men (N=9) and women (N=6), respectively. The grey arrow and black dotted lines show 24-hour, 7-day and 30-day locations along the x-axes.

Fig. 4

Examples of seizure occurrences locked to heart rate cycles for three participants. (a, b) P23 (416 seizures), (c, d) P31 (286 seizures), (e, f) P1 (105 seizures). (a, c, e) Heart rate (y-axis) and self-reported seizures (dots). A moving average (MA) filter was applied to heart rate (black line) to highlight cycles (a: 1-hour MA, c, e: 2-day MA). Panels (b, d, f) Corresponding circular histograms of the phase distributions of individuals’ heart rate cycles (white bins) showing the phase of seizure occurrences (shaded bins). Landmark phases are labelled as ‘peak’ (π/2), ‘trough’ (3π/2), ‘rising’ (2π) and ‘falling’ (π). Multiday circular histograms (Panels d, f) bins have the same phase width (2π/18) although these correspond to different durations (labelled by black arrows), depending on the period of the multiday cycle. The circadian histogram (Panel b) bins have widths of 1 hour (2π/24).

Fig. 5

Phase locking of seizures to heart rate cycles. Both subplots show individual heart rate cycles (arrows) with significant phase locking of seizure occurrence. The lengths of the arrows indicate the strength of phase locking, or SI (radial axis, between 0 and 1), while the direction indicates the preferred phase of seizure occurrence (polar axis). (a) Circadian cycles, all periods were 24 hours. (b) Multiday cycles (including about-weekly and about-monthly), 6– to 128-day periods (colour bar).