: We test the hypothesis that amplitude permutation conditional entropy (APCE) is more powerful than permutation conditional entropy (PCE) when complexity of heart period (HP) dynamics is decreased by vagal blockade or withdrawal. We acquired HP variability in 9 healthy male physicians (age: 25-46 yrs) at baseline (B) and during administration of a high dose of atropine (AT) and in 15 healthy nonsmoking volunteers (age: 24-54 yrs, 9 males and 6 females) at rest in horizontal position (T0) and during 90° head-up tilt (T90). In addition to coarse-graining-free methods, like PCE and APCE, we computed coarse-graining-based k-nearest-neighbor conditional entropy (KNNCE) for comparison. Markers were computed over 256 consecutive HP values, thus targeting the complexity of short-term cardiac control. PCE was unable to detect the decrease of HP variability complexity during AT compared to B, while APCE and KNNCE could. All the conditional entropy markers found a decrease in HP variability complexity during T90 compared to T0. Only APCE was correlated with KNNCE in both protocols. We conclude that APCE is more reliable than PCE in assessing cardiac control complexity, likely due to the better ability of APCE in the presence of the low signal-to-noise ratio of HP dynamics observed during AT.
Amplitude Permutation Conditional Entropy Detects the Decrease of Complexity of Heart Period Variability During Vagal Inhibition
Castiglioni P.Secondo
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2025-01-01
Abstract
: We test the hypothesis that amplitude permutation conditional entropy (APCE) is more powerful than permutation conditional entropy (PCE) when complexity of heart period (HP) dynamics is decreased by vagal blockade or withdrawal. We acquired HP variability in 9 healthy male physicians (age: 25-46 yrs) at baseline (B) and during administration of a high dose of atropine (AT) and in 15 healthy nonsmoking volunteers (age: 24-54 yrs, 9 males and 6 females) at rest in horizontal position (T0) and during 90° head-up tilt (T90). In addition to coarse-graining-free methods, like PCE and APCE, we computed coarse-graining-based k-nearest-neighbor conditional entropy (KNNCE) for comparison. Markers were computed over 256 consecutive HP values, thus targeting the complexity of short-term cardiac control. PCE was unable to detect the decrease of HP variability complexity during AT compared to B, while APCE and KNNCE could. All the conditional entropy markers found a decrease in HP variability complexity during T90 compared to T0. Only APCE was correlated with KNNCE in both protocols. We conclude that APCE is more reliable than PCE in assessing cardiac control complexity, likely due to the better ability of APCE in the presence of the low signal-to-noise ratio of HP dynamics observed during AT.
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