| Title |
Entropy change of biological dynamics in COPD
|
|---|---|
| Published in |
International Journal of Chronic Obstructive Pulmonary Disease, October 2017
|
| DOI | 10.2147/copd.s140636 |
| Pubmed ID | |
| Authors |
Yu Jin, Chang Chen, Zhixin Cao, Baoqing Sun, Iek Long Lo, Tzu-Ming Liu, Jun Zheng, Shixue Sun, Yan Shi, Xiaohua Douglas Zhang |
| Abstract |
In this century, the rapid development of large data storage technologies, mobile network technology, and portable medical devices makes it possible to measure, record, store, and track analysis of large amount of data in human physiological signals. Entropy is a key metric for quantifying the irregularity contained in physiological signals. In this review, we focus on how entropy changes in various physiological signals in COPD. Our review concludes that the entropy change relies on the types of physiological signals under investigation. For major physiological signals related to respiratory diseases, such as airflow, heart rate variability, and gait variability, the entropy of a patient with COPD is lower than that of a healthy person. However, in case of hormone secretion and respiratory sound, the entropy of a patient is higher than that of a healthy person. For mechanomyogram signal, the entropy increases with the increased severity of COPD. This result should give valuable guidance for the use of entropy for physiological signals measured by wearable medical device as well as for further research on entropy in COPD. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 46 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 13% |
| Student > Ph. D. Student | 5 | 11% |
| Professor > Associate Professor | 5 | 11% |
| Student > Bachelor | 4 | 9% |
| Student > Master | 4 | 9% |
| Other | 9 | 20% |
| Unknown | 13 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 11 | 24% |
| Medicine and Dentistry | 6 | 13% |
| Sports and Recreations | 4 | 9% |
| Nursing and Health Professions | 2 | 4% |
| Economics, Econometrics and Finance | 1 | 2% |
| Other | 5 | 11% |
| Unknown | 17 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 October 2017.
All research outputs
#16,051,091
of 25,382,440 outputs
Outputs from International Journal of Chronic Obstructive Pulmonary Disease
#1,486
of 2,578 outputs
Outputs of similar age
#188,166
of 331,218 outputs
Outputs of similar age from International Journal of Chronic Obstructive Pulmonary Disease
#39
of 70 outputs
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