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The study compares permutation and coarse-grained entropy approaches for the assessment of short-term complexity of the cardiovascular control from heart period (HP) variability recordings. Approaches were applied to simulations and to HP variability series recorded during complete vagal blockade obtained via administration of a high dose of atropine (AT). Differences between the estimated complexity indexes derived from both simulations and real data indicated that permutation entropy has a larger sensitivity to noise than coarse-grained entropy. We recommend caution in applying permutation entropy in presence of short HP variability series characterized by a low signal-to-noise ratio such as after complete vagal blockade induced by AT.

The mid-frequency (MF) component of the heart rate variability is an often used method of measuring changes in the level of mental effort during human-computer interaction. However, sometimes the exact cause of a change is not self explanatory. There are certain factors, aside from mental effort, that can influence the power level of the MF band: limb movement is one such factor. In this preliminary study, we examined the impact of relatively big arm movements on the power of the MF band. Participants played a game where they had to solve simple equations under a time pressure. In one of the conditions, they also had to raise their non-dominant hand every time they entered an answer. The analysis of our data seems to suggest that the kind of arm movements, used in the experiment, had no significant effect on the MF power. However, further exploration is required to identify what kinds of movements are capable of influencing this particular power band.

This study proposes an application of symbolic analysis to beat-to-beat heart rate variability data derived from 24 h Holter recordings both in healthy and heart failure populations. Heart rate series are transformed in a sequence of six symbols via a uniform quantization procedure. Symbols are grouped in patterns lasting three cardiac cycles and these patterns are grouped in few (four) families without any loss. The rate of occurrence of these families are found useful to distinguish two physiological conditions characterised by a different status of the autonomic nervous system (i.e. day-time and night-time) in healthy subjects and to discriminate between healthy and pathological populations. Indexes derived from symbolic analysis deserve to be added to traditional time and frequency domain parameters in standard analysis of heart rate variability obtained from 24 h Holter recordings and tested over larger databases

Regularity and synchronisation between different rhythms mainly at low (LF) and high frequency (HF) is evaluated in heart rate variability through conditional entropy (CE). CE quantifies the degree of information left after regular patterns have been removed. In short (about 300 points) data sequences CE is evaluated with a correction which considers as random the patterns that appear only one time and would otherwise be considered as regular. The minimum of the corrected CE (CCE) with increasing pattern length is chosen as the most reliable CE estimate. Beat-to-beat heart period series were analysed in 8 normal subjects at rest (R), head up tilt (T) and controlled respiration (CR). A significant decrease in CCE was found in T (0.9/spl plusmn/0.052) compared to R (1.01/spl plusmn/0.037); during CR, CCE displayed a slight and not significant increase (1.07/spl plusmn/0.055). The enhanced LF activity present in T seems to have a greater regularising power than the periodical input provided by CR at HF.