2005/10/28 by Leonardo Angelini, Angelini, L., Roberto Maestri +13
Engineering · #Data Analysis #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Physical sciences #Medical Physics (physics.med-ph) #Non-Invasive Vital Sign Monitoring #Quantitative Methods (q-bio.QM) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.physics/0510259
openalex publication_date 2005/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present the multiscale entropy analysis of short term physiological time series of simultaneously acquired samples of heart rate, blood pressure and lung volume, from healthy subjects and from subjects with Chronic Heart Failure. Evaluating the complexity of signals at the multiple time scales inherent in physiologic dynamics, we find that healthy subjects show more complex time series at large time scales; on the other hand, at fast time scales, which are more influenced by respiration, the pathologic dynamics of blood pressure is the most random. These results robustly separate healthy and pathologic groups. We also propose a multiscale approach to evaluate interactions between time series, by performing a multivariate autoregressive modelling of the coarse grained time series: this analysis provides several new quantitative indicators which are statistically correlated with the pathology.