Differentiating patients with obstructive sleep apnea from healthy controls based on heart rate-blood pressure coupling quantified by entropy-based indices.
Paweł PilarczykGrzegorz GraffJosé M AmigóKatarzyna TessmerAlistair S HallBeata GraffPublished in: Chaos (Woodbury, N.Y.) (2023)
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.
Keyphrases
- heart rate
- blood pressure
- machine learning
- obstructive sleep apnea
- heart rate variability
- big data
- deep learning
- end stage renal disease
- hypertensive patients
- positive airway pressure
- electronic health record
- artificial intelligence
- ejection fraction
- chronic kidney disease
- newly diagnosed
- prognostic factors
- peritoneal dialysis
- adipose tissue
- metabolic syndrome
- type diabetes
- sleep apnea
- skeletal muscle
- ionic liquid