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Differentiating patients with obstructive sleep apnea from healthy controls based on heart rate-blood pressure
Paweł Pilarczyk1, Grzegorz Graff2, José M Amigó3
1Faculty of Applied Physics and Mathematics and Digital Technologies Center, Gdańsk University of Technology, 80-233 Gdańsk, Poland.
Abstract:
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.
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