Multiscale Complexity Analysis of the Cardiac Control Identifies Asymptomatic and Symptomatic Patients in Long QT Syndrome Type 1

Abstract

The study assesses complexity of the cardiac control directed to the sinus node and to ventricles in long QT syndrome type 1 (LQT1) patients with KCNQ1-A341V mutation. Complexity was assessed via refined multiscale entropy (RMSE) computed over the beat-to-beat variability series of heart period (HP) and QT interval. HP and QT interval were approximated respectively as the temporal distance between two consecutive R-wave peaks and between the R-wave apex and T-wave end. Both measures were automatically taken from 24-hour electrocardiographic Holter traces recorded during daily activities in non mutation carriers (NMCs, n = 14) and mutation carriers (MCs, n = 34) belonging to a South African LQT1 founder population. The MC group was divided into asymptomatic (ASYMP, n = 11) and symptomatic (SYMP, n = 23) patients according to the symptom severity. Analyses were carried out during daytime (DAY, from 2PM to 6PM) and nighttime (NIGHT, from 12PM to 4AM) off and on beta-adrenergic blockade (BBoff and BBon). We found that the complexity of the HP variability at short time scale was under vagal control, being significantly increased during NIGHT and BBon both in ASYMP and SYMP groups, while the complexity of both HP and QT variability at long time scales was under sympathetic control, being smaller during NIGHT and BBon in SYMP subjects. Complexity indexes at long time scales in ASYMP individuals were smaller than those in SYMP ones regardless of therapy (i.e. BBoff or BBon), thus suggesting that a reduced complexity of the sympathetic regulation is protective in ASYMP individuals. RMSE analysis of HP and QT interval variability derived from routine 24-hour electrocardiographic Holter recordings might provide additional insights into the physiology of the cardiac control and might be fruitfully exploited to improve risk stratification in LQT1 population.

Glycemic Control, Hand Activity, and Complexity of Biological Signals in Diabetes Mellitus

“…The scale factor up to 10 was selected for a minimal length of the coarse-grained time series equal to 150 beats, a length appropriate for a reliable estimate of 퐸 [23]. As peripheral vasomotor rhythm is typically around 20 seconds, the time scale at = 1 was defined as small-scale for analyzing PPG pulse amplitudes series using the MSE algorithm [24]. In addition, the time scales, between 2 and 4, and between 5 and 10 were defined as medium-and large-scales, respectively, based on the study of Bari et al [24].…”

“…As peripheral vasomotor rhythm is typically around 20 seconds, the time scale at = 1 was defined as small-scale for analyzing PPG pulse amplitudes series using the MSE algorithm [24]. In addition, the time scales, between 2 and 4, and between 5 and 10 were defined as medium-and large-scales, respectively, based on the study of Bari et al [24].…”

“…The low pass filter's frequency response in MSE calculation poorly manifested by a slow roll-off of the main lobe, aliasing of important side lobes, and a large transition band may not be prevented as the filtered series is downsampled at a rate of one sample every [24]. A refined MSE (RMSE) method proposed by Valencia et al might improve the flaw of MSE [25].…”

Complex and Nonlinear Analysis of Heart Rate Variability in the Assessment of Fetal and Neonatal Wellbeing

“…LZ77 was modified by Baumert et al in 2004 [37] to analysis heart rate time series called the compression entropy (H c ). in the field of spontaneous fluctuations of cardiovascular oscillations entropy based methods were applied to investigate fetal development [38], to determine age effects on the autonomic system [39][40][41], in differentiating pathological states from healthy states [42][43][44][45][46], for monitoring cardiac autonomic function [34,47], in typifying the effects of a pharmacological treatment [48][49][50][51], and in predicting risk [37,52].…”

A Refined Multiscale Self-Entropy Approach for the Assessment of Cardiac Control Complexity: Application to Long QT Syndrome Type 1 Patients

“…While the CE is more widely utilized as a measure of complexity of a series [1][2][3], sE is traditionally exploited to assess regularity and predictability of a process [8] or information stored in it [7,9]. CE depends on time scales and this dependence, usually assessed via multiscale conditional entropy (MSCE) approach [11] or via its refinement referred to as refined MSCE (RMSCE) [12], provides relevant information about cardiovascular regulation because it allows the focalization of cardiac control mechanisms acting over an assigned time scale [11][12][13][14][15][16][17]. While the importance of monitoring the CE as a function of the time scale via MSCE or RMSCE is indubitable, it is unclear whether sE is worth to be monitored as a function of the time scale.…”