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

Abstract

Abstract:The study proposes the contemporaneous assessment of conditional entropy (CE) and self-entropy (sE), being the two terms of the Shannon entropy (ShE) decomposition, as a function of the time scale via refined multiscale CE (RMSCE) and sE (RMSsE) with the aim at gaining insight into cardiac control in long QT syndrome type 1 (LQT1) patients featuring the KCNQ1-A341V mutation. CE was estimated via the corrected CE (CCE) and sE as the difference between the ShE and CCE. RMSCE and OPEN ACCESSEntropy 2015, 17 7769 RMSsE were computed over the beat-to-beat series of heart period (HP) and QT interval derived from 24-hour Holter electrocardiographic recordings during daytime (DAY) and nighttime (NIGHT). LQT1 patients were subdivided into asymptomatic and symptomatic mutation carriers (AMCs and SMCs) according to the severity of symptoms and contrasted with non-mutation carriers (NMCs). We found that RMSCE and RMSsE carry non-redundant information, separate experimental conditions (i.e., DAY and NIGHT) within a given group and distinguish groups (i.e., NMC, AMC and SMC) assigned the experimental condition. Findings stress the importance of the joint evaluation of RMSCE and RMSsE over HP and QT variabilities to typify the state of the autonomic function and contribute to clarify differences between AMCs and SMCs.

Refined Multiscale Entropy Using Fuzzy Metrics: Validation and Application to Nociception Assessment

“…RMSE in its several variants was computed at different TS of the proposed synthetic and experimental time series. According to previous works [ 11 , 12 , 13 , 14 , 15 , 16 ], in this study r = 0.15 × SD and m = 2 were taken as fixed parameters (in SampEn and FuzEn approaches) to compare RMSE values of the synthetic series for different lengths N . In order to evaluate how the tolerance parameter r affects the performance of RMSE values of the EEG signals, this parameter was also varied between 0.10 and 0.30 in steps of 0.05.…”

“…(iii) When the Pk values were computed using FuzEn, the highest Pk values were obtained in timescales larger than TS = 10. Since EEG signals were resampled to 128 Hz, time scales between TS = 10 and TS = 20 represent a EEG signal with frequency components that reduces the superior limit of the pass-band from 6.4 (TS = 10) to 3.2 Hz (TS = 20), gradually removing contributions in the (14-30 Hz), (8)(9)(10)(11)(12)(13)(14), and (4-8 Hz) bands of the EEG, and leaving the fluctuations in the (0.1-4 Hz) band of the EEG. Since FuzEn and SampEn had the best Pk values at long time scales, Figure 5 was designed to show the mean values of the SampEn and FuzEn as a function of the time scales TS, obtained from the EEG segments divided into responsive (2 ≤ RSS ≤ 5) vs. unresponsive (RSS = 6) classes.…”

“…However, these measurements can be extended to provide a multiscale assessment of irregularity of the time series. Refined multiscale entropy (RMSE), proposed in [ 11 ], is a technique that uses SampEn as an entropy-based measure in order to quantify the complexity of a time series in different time scales, which has been applied in processing of electrocardiogram (ECG) and electroencephalogram (EEG) signals [ 12 , 13 , 14 , 15 ]. Computation of RMSE is similar to multiscale entropy (MSE) [ 16 ] except for two significant modifications: (i) RMSE improves the procedure applied to remove the fast time scales in the signal, avoiding the aliasing, and; (ii) it modifies the coarse-graining procedure to avoid an artificial decrease of the entropy as the fast time scales in the signal are eliminated, which is caused by the reduction of the standard deviation that is generated by the filtering process.…”

Autonomic Control of the Heart and Its Clinical Impact. A Personal Perspective

“…In a founder population of long QT syndrome type 1 (LQT1), which avoids the confounding factors due to different mutations and segregates the malignant KCNQ1 -A341V mutation ( Brink et al, 2005 ; Crotti et al, 2007 ; Brink and Schwartz, 2009 ), the characterization of cardiac autonomic control and baroreflex function was found to be useful to improve the risk stratification of arrhythmic events ( Schwartz et al, 2008b ; Crotti et al, 2012 ; Bari et al, 2014a , b , 2015 ; Porta et al, 2015 ). In this population which is at the highest risk of fatal events in situations of high sympathetic drive ( Schwartz et al, 2001 ), it was found that subjects who did not experience arrhythmic events, namely the asymptomatic mutation-carriers, have a completely different autonomic profile compared to those experiencing syncope or cardiac arrest requiring resuscitation.…”

“…In this population which is at the highest risk of fatal events in situations of high sympathetic drive ( Schwartz et al, 2001 ), it was found that subjects who did not experience arrhythmic events, namely the asymptomatic mutation-carriers, have a completely different autonomic profile compared to those experiencing syncope or cardiac arrest requiring resuscitation. Indeed, asymptomatic individuals exhibited longer RR ( Schwartz et al, 2008b ), lower BRS ( Schwartz et al, 2008b ), higher QT variability in the LF band during daytime ( Porta et al, 2015 ), lower respiratory sinus arrhythmia during nighttime and under β-blockers ( Porta et al, 2015 ), slower heart rate recovery after exercise test ( Crotti et al, 2012 ), and lower QT variability complexity ( Bari et al, 2014a , b , 2015 ). These findings suggested that, besides RR lengthening, the combination of a more reactive sympathetic drive to the ventricles (i.e., adapting more rapidly QT to RR changes and limiting irregularity of QT changes during a sympathetic stressor) and of a sluggish vagal responsiveness after exercise represents a protective mechanism.…”