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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
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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Authors (7)
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Krzysztof Narkiewicz
-
Beata Graff
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- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1063/5.0158923
- License
- Copyright (2023 AIP Publishing)
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
CHAOS
pages 1 - 13,
ISSN: 1054-1500 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Pilarczyk P., Graff G., Amigo J., Tessmer K., Narkiewicz K., Graff B.: Differentiating patients with obstructive sleep apnea from healthy controls based on heart rate-blood pressure coupling quantified by entropy-based indices// CHAOS -Vol. 10,iss. art id 103140 (2023), s.1-13
- DOI:
- Digital Object Identifier (open in new tab) 10.1063/5.0158923
- Sources of funding:
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- Free publication
- Verified by:
- Gdańsk University of Technology
Referenced datasets
- dataset Cross-validation for triplets of HRV and BPV indices based on ordinal patterns in differentiating OSA patients from healthy controls
- dataset Cross-validation for triplets of classical HRV and BPV indices in differentiating OSA patients from healthy controls
- dataset HRV and BPV indices in differentiating OSA patients from healthy controls
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