Showing 2 results for Entropy
Saeed Pazhoohan, Fatemeh Abbasi Feijani, Abdollatif Moini , ,
Volume 26, Issue 6 (2-2024)
Abstract
Chronic obstructive pulmonary disease (COPD) is a progressive and debilitating condition. People with COPD often experience exacerbations that may require hospitalization. Pattern analysis of respiratory variability can provide valuable insights into the complexity of the respiratory control system. Therefore, this study investigated respiratory pattern variability during stable and exacerbation phases in patients with COPD.
We analyzed respiratory signal data from 14 patients with COPD during exacerbations and stable phases and compared them with 12 age- and sex-matched control subjects. Respiratory pattern variability analysis of 30-minute inter-breath intervals (IBI) time series was performed using sample entropy and Detrended Fluctuation Analysis.
Sample entropy analysis of the IBI revealed that respiratory variability was more regular during both stable and exacerbation phases in patients with COPD. Also, the short-term (α1) and long-term fractal-like correlation (α2) significantly decreased during both exacerbation and stable phases compared to healthy controls.
The respiratory control system in patients with COPD shows less variability (lower entropy and fractal correlation). This reduction in respiratory signal variability indices in COPD patients is still lower than in healthy people, even when their disease status is stabilized.
Abdollatif Moini, Saeed Pazhoohan,
Volume 28, Issue 5 (12-2025)
Abstract
Abstract
Introduction: Chronic obstructive pulmonary disease (COPD) is a common condition characterized by airflow limitation in the lungs, which is associated with numerous respiratory and cardiovascular complications. The aim of this study was to examine changes in pulse rate and signal complexity indices of the pulse in patients with COPD during hospitalization and compare them with healthy individuals.
Methods: In this study, the variability of inter-pulse interval time series over a 30-minute period was analyzed using Multiscale Entropy (MSE) and Detrended Fluctuation Analysis (DFA) methods. The pulse oximetry data of 15 hospitalized patients with COPD during their hospitalization and discharge were examined and compared with data from 21 age- and sex-matched control subjects.
Results: The results showed that the pulse rate of patients diagnosed with COPD was significantly higher both during hospitalization and after discharge compared to the control group (P < 0.001). In addition, complexity indices such as entropy (P < 0.05) and fractal correlation (P < 0.01) were significantly reduced in these patients. These changes were observed both during hospitalization, when patients experienced symptom exacerbation, and at discharge, when their clinical condition had stabilized.
Conclusions: The results showed that the pulse signals of patients with COPD have reduced complexity and flexibility. Assessing changes in the complexity of these signals may serve as a valuable tool for monitoring and managing the condition of patients at different stages of the disease. Such methods may enable early detection of disease exacerbations and cardiac complications, thereby contributing to improved treatment outcomes and a reduction in disease-related complications.
