{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T04:59:38Z","timestamp":1778821178356,"version":"3.51.4"},"reference-count":49,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,5,24]],"date-time":"2018-05-24T00:00:00Z","timestamp":1527120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Macau Start-up Research Grant","award":["SRG2016-00083-FHS"],"award-info":[{"award-number":["SRG2016-00083-FHS"]}]},{"name":"University of Macau FHS Research Grant","award":["FHS-CRDA-029-002-2017"],"award-info":[{"award-number":["FHS-CRDA-029-002-2017"]}]},{"DOI":"10.13039\/501100006469","name":"Fundo para o Desenvolvimento das Ci\u00eancias e da Tecnologia","doi-asserted-by":"publisher","award":["FDCT\/131\/2016\/A3"],"award-info":[{"award-number":["FDCT\/131\/2016\/A3"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e20060402","type":"journal-article","created":{"date-parts":[[2018,5,24]],"date-time":"2018-05-24T07:54:01Z","timestamp":1527148441000},"page":"402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Entropy Change of Biological Dynamics in Asthmatic Patients and Its Diagnostic Value in Individualized Treatment: A Systematic Review"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0929-977X","authenticated-orcid":false,"given":"Shixue","family":"Sun","sequence":"first","affiliation":[{"name":"Faculty of Health Sciences, University of Macau, Taipa, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3290-3293","authenticated-orcid":false,"given":"Yu","family":"Jin","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences, University of Macau, Taipa, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences, University of Macau, Taipa, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baoqing","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Respiratory Disease, the 1st Affiliated Hospital of Guangzhou Medical University, Guangzhou 510230, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixin","family":"Cao","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Iek","family":"Lo","sequence":"additional","affiliation":[{"name":"Department of Geriatrics, Centro Hospital Conde de Sao Januario, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences, University of Macau, Taipa, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Zheng","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences, University of Macau, Taipa, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Electronic Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2486-7931","authenticated-orcid":false,"given":"Xiaohua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences, University of Macau, Taipa, Macau, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,24]]},"reference":[{"key":"ref_1","unstructured":"(2017, March 12). Pocket Guide for Asthma Management and Prevention. Available online: http:\/\/ginasthma.org\/wp-content\/uploads\/2016\/01\/GINA_Pocket_2015.pdf."},{"key":"ref_2","unstructured":"World Health Organization (2017, March 12). Asthma. Available online: http:\/\/www.who.int\/mediacentre\/factsheets\/fs307\/en\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1183\/09031936.00088814","article-title":"Monitoring asthma in children","volume":"45","author":"Pijnenburg","year":"2015","journal-title":"Eur. Respir. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1152\/jappl.2001.91.3.1131","article-title":"Homeokinesis and short-term variability of human airway caliber","volume":"91","author":"Que","year":"2001","journal-title":"J. Appl. Physiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1038\/nature04176","article-title":"Risk of severe asthma episodes predicted from fluctuation analysis of airway function","volume":"438","author":"Frey","year":"2005","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","article-title":"Approximate entropy as a measure of system complexity","volume":"88","author":"Pincus","year":"1991","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2083","DOI":"10.1073\/pnas.93.5.2083","article-title":"Randomness and degrees of irregularity","volume":"93","author":"Pincus","year":"1996","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.3390\/e17031197","article-title":"Generalized multiscale entropy analysis: Application to quantifying the complex volatility of human heartbeat time series","volume":"17","author":"Costa","year":"2015","journal-title":"Entropy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"021906","DOI":"10.1103\/PhysRevE.71.021906","article-title":"Multiscale entropy analysis of biological signals","volume":"71","author":"Costa","year":"2005","journal-title":"Phys. Rev. E Stat. Nonlinear Soft Matter Phys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2997","DOI":"10.2147\/COPD.S140636","article-title":"Entropy change of biological dynamics in COPD","volume":"12","author":"Jin","year":"2017","journal-title":"Int. J. Chron. Obstr. Pulm. Dis."},{"key":"ref_12","first-page":"3","article-title":"CGManalyzer: An R package for analyzing continuous glucose monitoring studies","volume":"1","author":"Zhang","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5218","DOI":"10.3390\/e17085218","article-title":"An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures","volume":"17","author":"Sharma","year":"2015","journal-title":"Entropy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1378\/chest.106.5.1432","article-title":"Decreased heart rate variability in patients with chronic obstructive pulmonary disease","volume":"106","author":"Volterrani","year":"1994","journal-title":"Chest"},{"key":"ref_15","first-page":"200","article-title":"C-reactive protein, lung hyperinflation and heart rate variability in chronic obstructive pulmonary disease\u2014A pilot study","volume":"10","author":"Corbo","year":"2013","journal-title":"COPD J. Chron. Obstr. Pulm. Dis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.7150\/ijbs.19462","article-title":"Complexity change in cardiovascular disease","volume":"13","author":"Chen","year":"2017","journal-title":"Int. J. Biol. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.2147\/COPD.S108860","article-title":"Respiratory muscle strength effect on linear and nonlinear heart rate variability parameters in COPD patients","volume":"11","author":"Simon","year":"2016","journal-title":"Int. J. Chron. Obstr. Pulm. Dis."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.resp.2013.12.004","article-title":"Airflow pattern complexity during resting breathing in patients with COPD: Effect of airway obstruction","volume":"192","author":"Dames","year":"2014","journal-title":"Respir. Physiol. Neurobiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"246","DOI":"10.3109\/02770903.2014.957765","article-title":"Increased sympathetic modulation and decreased response of the heart rate variability in controlled asthma","volume":"52","author":"Labadessa","year":"2015","journal-title":"J. Asthma"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1183\/09031936.00228611","article-title":"Airway impedance entropy and exacerbations in severe asthma","volume":"40","author":"Gonem","year":"2012","journal-title":"Eur. Respir. J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Veiga, J., Faria, R.C., Esteves, G.P., Lopes, A.J., Jansen, J.M., and Melo, P.L. (September, January 31). Approximate entropy as a measure of the airflow pattern complexity in asthma. Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Buenos Aires, Argentina.","DOI":"10.1109\/IEMBS.2010.5626547"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1007\/s11517-012-0957-x","article-title":"Fluctuation analysis of respiratory impedance waveform in asthmatic patients: Effect of airway obstruction","volume":"50","author":"Veiga","year":"2012","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.apjtb.2015.10.001","article-title":"Urban air pollution, climate and its impact on asthma morbidity","volume":"6","author":"Veremchuk","year":"2016","journal-title":"Asian Pac. J Trop. Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Aydore, S., Sen, I., Kahya, Y.P., and Mihcak, M.K. (2009, January 3\u20136). Classification of respiratory signals by linear analysis. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, Minneapolis, MN, USA.","DOI":"10.1109\/IEMBS.2009.5335395"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0197-2456(95)00134-4","article-title":"Assessing the quality of reports of randomized clinical trials: Is blinding necessary?","volume":"17","author":"Jadad","year":"1996","journal-title":"Control. Clin. Trials"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1023\/A:1004939118378","article-title":"Boltzmann entropy: Generalization and applications","volume":"23","author":"Chakrabarti","year":"1997","journal-title":"J. Biol. Phys."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1002\/j.1538-7305.1949.tb00928.x","article-title":"Communication theory of secrecy systems","volume":"28","author":"Shannon","year":"1949","journal-title":"Bell Labs Tech. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2591","DOI":"10.1103\/PhysRevA.28.2591","article-title":"Estimation of the kolmogorov entropy from a chaotic signal","volume":"28","author":"Grassberger","year":"1983","journal-title":"Phys. Rev. A"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1002\/1097-0193(200009)11:1<46::AID-HBM40>3.0.CO;2-5","article-title":"Measuring the complexity of time series: An application to neurophysiological signals","volume":"11","author":"Thut","year":"2000","journal-title":"Hum. Brain Mapp."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1063\/1.166492","article-title":"Entropy computing via integration over fractal measures","volume":"10","author":"Slomczynski","year":"2000","journal-title":"Chaos"},{"key":"ref_31","first-page":"170","article-title":"The mathematical theory information","volume":"97","author":"Shannon","year":"1949","journal-title":"Math. Gazette"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1016\/j.compbiomed.2007.01.014","article-title":"A diagnostic software tool for determination of complexity in respiratory pattern parameters","volume":"37","year":"2007","journal-title":"Comput. Biol. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1164\/rccm.200611-1739OC","article-title":"Linking parenchymal disease progression to changes in lung mechanical function by percolation","volume":"176","author":"Bates","year":"2007","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1038\/nature03490","article-title":"Self-organized patchiness in asthma as a prelude to catastrophic shifts","volume":"434","author":"Venegas","year":"2005","journal-title":"Nature"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1152\/japplphysiol.00267.2011","article-title":"Airflow pattern complexity and airway obstruction in asthma","volume":"111","author":"Veiga","year":"2011","journal-title":"J. Appl. Physiol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Raoufy, M.R., Ghafari, T., Darooei, R., Nazari, M., Mahdaviani, S.A., Eslaminejad, A.R., Almasnia, M., Gharibzadeh, S., Mani, A.R., and Hajizadeh, S. (2016). Classification of asthma based on nonlinear analysis of breathing pattern. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0147976"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"191","DOI":"10.3109\/02770903.2014.954290","article-title":"Characteristics of postural control among young adults with asthma","volume":"52","author":"Kuznetsov","year":"2015","journal-title":"J. Asthma"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jin, F., Sattar, F., and Goh, D.Y. (2008, January 20\u201325). Automatic wheeze detection using histograms of sample entropy. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008, Vancouver, BC, Canada.","DOI":"10.1109\/IEMBS.2008.4649555"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"182938","DOI":"10.1155\/2014\/182938","article-title":"Detection of lungs status using morphological complexities of respiratory sounds","volume":"2014","author":"Mondal","year":"2014","journal-title":"Sci. World J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1183\/09031936.03.00089403","article-title":"The forced oscillation technique in clinical practice: Methodology, recommendations and future developments","volume":"22","author":"Oostveen","year":"2003","journal-title":"Eur. Respir. J."},{"key":"ref_41","first-page":"A129","article-title":"The use of impulse oscillometry (IOS) to study fractal scaling and sample entropy in airway resistance time series in severe asthma","volume":"65","author":"Umar","year":"2010","journal-title":"Thorax"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1038\/nbt.3495","article-title":"Digital medicine\u2019s march on chronic disease","volume":"34","author":"Kvedar","year":"2016","journal-title":"Nat. Biotechnol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1038\/nbt.3222","article-title":"Defining digital medicine","volume":"33","author":"Elenko","year":"2015","journal-title":"Nat. Biotechnol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1353\/pbm.1997.0063","article-title":"Fractal variability versus pathologic periodicity: Complexity loss and stereotypy in disease","volume":"40","author":"Goldberger","year":"1997","journal-title":"Perspect. Biol. Med."},{"key":"ref_45","unstructured":"Kaguara, A., Myoung Nam, K., and Reddy, S. (2018, April 11). A Deep Neural Network Classifier for Diagnosing Sleep Apnea from ECG Data on Smartphones and Small Embedded Systems. Available online:https:\/\/www.researchgate.net\/publication\/273633242_A_deep_neural_network_classifier_for_diagnosing_sleep_apnea_from_ECG_data_on_smartphones_and_small_embedded_systems."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ins.2017.04.012","article-title":"Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network","volume":"405","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1093\/bioinformatics\/btx652","article-title":"Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques","volume":"34","author":"Niu","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_48","unstructured":"Shi, Y., Wang, G., Niu, J., Zhang, Q., Cai, M., Sun, B., Wang, D., Xue, M., and Zhang, X.D. Classification of sputum sounds using artificial neural network and wavelet transform. Int. J. Biol. Sci., in press."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2018.03.011","article-title":"A method to detect sleep apnea based on deep neural network and hidden markov model using single-lead ECG signal","volume":"294","author":"Li","year":"2018","journal-title":"Neurocomputing"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/6\/402\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:05:45Z","timestamp":1760195145000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/6\/402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,24]]},"references-count":49,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["e20060402"],"URL":"https:\/\/doi.org\/10.3390\/e20060402","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,24]]}}}