Complexity reduction of oxygen saturation variability signals in COVID-19 patients: Implications for cardiorespiratory control

Madini O Alassafi; Wajid Aziz; Rayed AlGhamdi; Abdulrahman A Alshdadi; Malik Sajjad Ahmed Nadeem; Ishtiaq Rasool Khan; Adel Bahaddad; Ali Altalbe; Nabeel Albishry

doi:10.1016/j.jiph.2024.02.004

Complexity reduction of oxygen saturation variability signals in COVID-19 patients: Implications for cardiorespiratory control

J Infect Public Health. 2024 Apr;17(4):601-608. doi: 10.1016/j.jiph.2024.02.004. Epub 2024 Feb 9.

Authors

Madini O Alassafi¹, Wajid Aziz², Rayed AlGhamdi³, Abdulrahman A Alshdadi⁴, Malik Sajjad Ahmed Nadeem², Ishtiaq Rasool Khan⁵, Adel Bahaddad¹, Ali Altalbe¹, Nabeel Albishry¹

Affiliations

¹ Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
² Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, AK, Pakistan.
³ Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. Electronic address: raalghamdi8@kau.edu.sa.
⁴ Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia.
⁵ Abu Dhabi School of Management, United Arab Emirates.

PMID: 38377633
DOI: 10.1016/j.jiph.2024.02.004

Abstract

Background: Coronavirus disease 2019 (COVID-19) is a respiratory illness that leads to severe acute respiratory syndrome and various cardiorespiratory complications, contributing to morbidity and mortality. Entropy analysis has demonstrated its ability to monitor physiological states and system dynamics during health and disease. The main objective of the study is to extract information about cardiorespiratory control by conducting a complexity analysis of OSV signals using scale-based entropy measures following a two-month timeframe after recovery.

Methods: This prospective study collected data from subjects meeting specific criteria, using a Beurer PO-80 pulse oximeter to measure oxygen saturation (SpO2) and pulse rate. Excluding individuals with a history of pulmonary/cardiovascular issues, the study analyzed 88 recordings from 44 subjects (26 men, 18 women, mean age 45.34 ± 14.40) during COVID-19 and two months post-recovery. Data preprocessing and scale-based entropy analysis were applied to assess OSV signals.

Results: The study found a significant difference in mean OSV during illness (95.08 ± 0.15) compared to post-recovery (95.59 ± 1.03), indicating reduced cardiorespiratory dynamism during COVID-19. Multiscale entropy analyses (MSE, MPE, MFE) confirmed lower entropy values during illness across all time scales, particularly at higher scales. Notably, the maximum distinction between illness and recovery phases was seen at specific time scales and similarity criteria for each entropy measure, showing statistically significant differences.

Conclusions: The study demonstrates that the loss of complexity in OSV signals, quantified using scale-based entropy measures, has the potential to detect malfunctioning of cardiorespiratory control in COVID-19 patients. This finding suggests that OSV signals could serve as a valuable indicator for assessing the cardiorespiratory status of COVID-19 patients and monitoring their recovery progress.

Keywords: COVID-19; Cardiorespiratory control system; Complexity analysis; Oxygen saturation variability; Scale-based entropy.

MeSH terms

Adult
COVID-19*
Female
Humans
Male
Middle Aged
Oxygen Saturation
Prospective Studies