Entropy Change of Biological Dynamics in Asthmatic Patients and Its Diagnostic Value in Individualized Treatment: A Systematic Review

Entropy

Zhixin Cao¹  Iek Long Lo²  Yan Shi³  Qi Zhao⁴  Chang Chen⁴  Xiaohua Douglas Zhang⁴  Shixue Sun⁴  Yu Jin⁴  Jun Zheng⁴  Baoqing Sun⁵ 

[1] Beijing Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China;Department of Geriatrics, Centro Hospital Conde de Sao Januario, Macau, China;Department of Mechanical and Electronic Engineering, Beihang University, Beijing 100191, China;Faculty of Health Sciences, University of Macau, Taipa, Macau, China;State Key Laboratory of Respiratory Disease, the 1st Affiliated Hospital of Guangzhou Medical University, Guangzhou 510230, China;

关键词: entropy;    irregularity;    asthma;    physiological signal;    individualized treatment;   

DOI  :  10.3390/e20060402

来源: DOAJ

【 摘 要 】

Asthma is a chronic respiratory disease featured with unpredictable flare-ups, for which continuous lung function monitoring is the key for symptoms control. To find new indices to individually classify severity and predict disease prognosis, continuous physiological data collected from monitoring devices is being studied from different perspectives. Entropy, as an analysis method for quantifying the inner irregularity of data, has been widely applied in physiological signals. However, based on our knowledge, there is no such study to summarize the complexity differences of various physiological signals in asthmatic patients. Therefore, we organized a systematic review to summarize the complexity differences of important signals in patients with asthma. We searched several medical databases and systematically reviewed existing asthma clinical trials in which entropy changes in physiological signals were studied. As a conclusion, we find that, for airflow, heart rate variability, center of pressure and respiratory impedance, their entropy values decrease significantly in asthma patients compared to those of healthy people, while, for respiratory sound and airway resistance, their entropy values increase along with the progression of asthma. Entropy of some signals, such as respiratory inter-breath interval, shows strong potential as novel indices of asthma severity. These results will give valuable guidance for the utilization of entropy in physiological signals. Furthermore, these results should promote the development of management and diagnosis of asthma using continuous monitoring data in the future.

【 授权许可】

Unknown