A Multicentric Study of Long:term Rhythm Patterns in Heart Rate

Abstract

Heart rate (HR) shows oscillations with different periods as a result of the sympatho-vagal balance. The most studied ones are short-period variations and the circadian pattern. However, the existence of rhythms of longer periods has not been systematically studied.The aim of this work is to study HR long-period rhythms in a multicentric database including 336 patients in sinus rhythm, with implanted cardioverter defibrillator. We used two methodological approaches: First a rhythmometric procedure to automatically select the statistically significant rhythms present in the signal; Second, the LASSO path approach to analyze the order of activation of the rhythms, representing the importance of each rhythm.Most of the population showed a significant annual rhythm (78% day/80% night). Weekly and quarterly rhythms were also present (weekly 26% /26% ; quarterly 22%/21%). Monthly rhythm was rarely present. Most present rhythm combinations were annual plus weekly (21%/21%) and annual plus quarterly (19%/18%). The order of activation given by the LASSO path was in agreement with the multicomponent rhythmometric model in the 86%/85% of the cases.The unusual long monitoring period, and the high number of patients, represent an ideal scenario to robustly assess the existence of long-term rhythms.

Epileptic Seizure Cycles: Six Common Clinical Misconceptions

“…Evidence for monthly rhythms in biological phenomena is limited but includes sleep quality ( 78 ) and hormone levels, including testosterone ( 79 ). Monthly rhythms can also be found in heart rate, but this is likely to be rare; one study mentioned about 3% of people ( 80 ). Within populations, weak seasonal cycles have been observed in some physiological states and biomarkers, including human cognition ( 81 ), skin temperature ( 82 ), and salivary cortisol ( 83 ).…”

A Group Lasso Based Method for Automatic Physiological Rhythm Analysis

“…In [7] we presented a lasso path approach to analyze the order of activation of the rhythms, representing the importance of each rhythm, in HR signals. In the present work, we further develop the approach proposing an automatic rhythm analysis method based on lasso or l 1 -regularized linear regression, with physiological rhythm components as features.…”