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

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Differentiating patients with obstructive sleep apnea from healthy  controls based on heart rate - blood pressure coupling quantified by  entropy-based indices