Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, AK, Pakistan.
J Infect Public Health. 2024 Apr;17(4):601-608. doi: 10.1016/j.jiph.2024.02.004. Epub 2024 Feb 9.
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.
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.
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.
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.
2019 年冠状病毒病(COVID-19)是一种导致严重急性呼吸系统综合征和各种心肺并发症的呼吸道疾病,导致发病率和死亡率上升。熵分析已经证明了其在健康和疾病期间监测生理状态和系统动态的能力。本研究的主要目的是通过对 OSV 信号进行基于尺度的熵测量复杂性分析,从 COVID-19 康复后两个月的时间框架内提取心肺控制信息。
本前瞻性研究从符合特定标准的受试者中收集数据,使用 Beurer PO-80 脉搏血氧仪测量血氧饱和度(SpO2)和脉搏率。排除有肺部/心血管问题病史的个体,该研究分析了 44 名受试者(26 名男性,18 名女性,平均年龄 45.34±14.40 岁)的 88 次记录,这些记录是在 COVID-19 期间和康复后两个月内采集的。对 OSV 信号进行数据预处理和基于尺度的熵分析。
研究发现,患病期间的平均 OSV(95.08±0.15)与康复后相比显著降低(95.59±1.03),表明 COVID-19 期间心肺动态性降低。多尺度熵分析(MSE、MPE、MFE)证实,在所有时间尺度上,患病期间的熵值均较低,特别是在较高的尺度上。值得注意的是,在特定的时间尺度和每个熵测度的相似性标准下,疾病和康复阶段之间的最大区别是可以看到的,表现出统计学上的显著差异。
该研究表明,使用基于尺度的熵测量来量化 OSV 信号的复杂性损失,有可能检测 COVID-19 患者心肺控制的故障。这一发现表明,OSV 信号可以作为评估 COVID-19 患者心肺状态和监测其康复进展的有价值指标。