P. Pilarcyk (Faculty of Applied Physics and Mathematics and Digital Technologies Center, Gdańsk University of Technology), G. Graff (Faculty of Applied Physics and Mathematics and BioTechMed Center, Gdańsk University of Technology), J.M. Amigó (Operations Research Center, University Miguel Hernández of Elche), K. Tessmer (Faculty of Applied Physics and Mathematics, Gdańsk University of Technology), K. Narkiewicz (Department of Hypertension and Diabetology, Medical University of Gdańsk) and B. Graffand (Department of Hypertension and Diabetology, Medical University of Gdańsk)
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.