, we have averaged our results over large numbers of (normal or acidotic) subjects, which jeopardizes any precise interpretation in terms of a specific FHR
, In addition, the performance slightly increases when the total embedding dimension m+p increases; although one has to care about the curse of dimensionality. For abnormal subjects from Dataset II, AMI is not able to detect acidosis using data from Stage 1. This suggests that acidosis develops later, in the second stage of labor. For all datasets, AMI computed with ? = 0.5 s is always larger for acidotic subjects than for normal subjects. This is in agreement with results obtained with ApEn and SampEn, which are both lower for acidotic subjects. This shows that FHR classified as abnormal have a stronger dependence structure at a small scale than normal ones. We can relate this increase of the dependence structure of acidotic FHR to the shortterm variability and to its coupling with particular large-scale patterns. For example, a sinusoidal FHR pattern [14], especially if its duration is long, should give a larger value of the AMI, because its large-scale dynamics is highly predictable. As another example, we expect variable decelerations (with an asymmetrical V-shape) and late decelerations (with a symmetrical U-shape and/or reduced variability) to impact AMI differently. Of course, the choice of the embedding parameter ? is then crucial, and this is currently under investigation. AMI and entropy rates depend on the dynamics as they operate on time-embedded vectors, Shannon entropy strongly depends on the standard deviation of the signal (e.g., see eq.(1.38)), which in turn depends on the variability in the observation window
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