Bayesowska entropia częstości serca identyfikuje dynamikę autonomiczną podczas ewolucji napadu epilepsji: powtarzalny projekt pilotażowy

Epileptic seizures are accompanied by profound alterations in autonomic regulation, yet the physiological information encoded in cardiac dynamics remains incompletely understood. Bayesian heart-rate (HR) entropy has recently been proposed as a probabilistic measure of cardiac dynamics, but whether it captures clinically meaningful aspects of seizure physiology, such as behavioral awareness or seizure evolution, has not been established. We estimated Bayesian HR entropy from electrocardiographic recordings acquired during video-electroencephalographic monitoring using the BayesianAtHeart framework. Entropy-derived measures were integrated with quality-controlled clinical metadata to generate a frozen seizure-level analysis dataset, from which all subsequent analyses were performed. Associations between seizure-average Bayesian HR entropy and clinical variables were evaluated using linear mixed-effects models accounting for repeated seizures within patients, and time-resolved entropy trajectories were analyzed descriptively. Following ECG quality control, Bayesian HR entropy was successfully estimated for 51 of 67 seizures from 10 patients; the remaining 16 seizures were excluded because ECG quality was insufficient. Forty-eight seizures with complete awareness classification comprised the primary analysis cohort. Bayesian HR entropy was not associated with ictal awareness across seizure-average analyses, mixed-effects models, or time-resolved entropy trajectories. Instead, seizure duration emerged as the strongest clinical correlate of Bayesian HR entropy, with longer seizures exhibiting progressively lower Bayesian HR entropy. Time-resolved analyses indicated that this association reflected a gradual decline in entropy during seizure evolution rather than lower Bayesian HR entropy at seizure onset. Post hoc sensitivity analyses showed that this association was not attributable to selection bias or to the number of beat-to-beat intervals available to the entropy estimator, and that duration, rather than stable between-patient differences, was the dominant source of entropy variance. Entropy was not generally reduced during seizures relative to a pre-ictal baseline; instead, the variability of the entropy trajectory declined progressively with seizure duration, more strongly than its mean. These findings suggest that Bayesian HR entropy primarily reflects the evolving organization of autonomic regulation during seizures rather than behavioral awareness. Beyond identifying seizure duration as the strongest correlate of Bayesian HR entropy in this cohort, this study establishes a fully reproducible computational framework for Bayesian HR entropy analysis that provides a foundation for future prospective investigations of autonomic dynamics in epilepsy.