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 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.
Highlights
Information domain analysis of time series provides relevant information about the behavior of complex physiological systems [1,2,3,4,5,6,7]
As a result of the pathology μQT was longer in asymptomatic MCs (AMCs) and symptomatic MCs (SMCs) during both DAY and NIGHT, and μQT increased during NIGHT regardless of the group
The present study proposes a multiscale approach of the two portions of the Shannon entropy (ShE), i.e., conditional entropy (CE) and sE, with the major aim at characterizing the complexity of cardiac control and improving risk stratification in long QT syndrome type 1 (LQT1) via the analysis of heart period (HP) and QT variabilities derived from 24-hour electrocardiographic recordings acquired routinely as a part of the monitoring of this pathological population
Summary
Information domain analysis of time series provides relevant information about the behavior of complex physiological systems [1,2,3,4,5,6,7]. In this domain indexes of dynamical complexity are naturally quantified via self-entropy (sE) and conditional entropy (CE), measuring the portions of the amount of information about the current variable that can and cannot be derived from previous past values of the same variable respectively. The higher the sE, the higher the predictability and regularity of the series is. 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]
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