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

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 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.

Automated summary

The paper introduces an entropy-based classification method for quantifying mutual dependencies in heart rate and beat-to-beat blood pressure recordings. Machine learning is used to select a subset of suitable indices to build an optimal model for distinguishing patients with obstructive sleep apnea from a control group.