Differentiating patients with obstructive sleep apnea from healthy controls based on heart rate - blood pressure coupling quantified by entropy-based indices
This work addresses a domain-specific medical diagnosis problem, offering an incremental improvement in classification techniques for sleep apnea detection.
The authors tackled the problem of distinguishing obstructive sleep apnea patients from healthy controls by developing an entropy-based classification method for heart rate and blood pressure coupling, achieving a classification model optimized via machine learning.
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 the 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.