  We introduce an entropy-based classification method for pairs of sequences
(ECPS) for quantifying mutual dependencies in heart rate and beat-to-beat blood
pressure recordings. The purpose of the method is to build a classifier for
data in which each item consists of the two intertwined data series taken for
each subject. The method is based on ordinal patterns, and uses entropy-like
indices. Machine learning is used to select a subset of indices most suitable
for our classification problem in order to build an optimal yet simple model
for distinguishing between patients suffering from obstructive sleep apnea and
a control group.
