{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T14:43:42Z","timestamp":1767969822930,"version":"3.49.0"},"reference-count":28,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,11]],"date-time":"2018-12-11T00:00:00Z","timestamp":1544486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information and communications Technology Promotion","doi-asserted-by":"publisher","award":["2017-0-00167"],"award-info":[{"award-number":["2017-0-00167"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002643","name":"Kwangwoon University","doi-asserted-by":"publisher","award":["2017"],"award-info":[{"award-number":["2017"]}],"id":[{"id":"10.13039\/501100002643","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Electrocardiogram (ECG) signal has been commonly used to analyze the complexity of heart rate variability (HRV). For this, various entropy methods have been considerably of interest. The multiscale entropy (MSE) method, which makes use of the sample entropy (SampEn) calculation of coarse-grained time series, has attracted attention for analysis of HRV. However, the SampEn computation may fail to be defined when the length of a time series is not enough long. Recently, distribution entropy (DistEn) with improved stability for a short-term time series has been proposed. Here, we propose a novel multiscale DistEn (MDE) for analysis of the complexity of short-term HRV by utilizing a moving-averaging multiscale process and the DistEn computation of each moving-averaged time series. Thus, it provides an improved stability of entropy evaluation for short-term HRV extracted from ECG. To verify the performance of MDE, we employ the analysis of synthetic signals and confirm the superiority of MDE over MSE. Then, we evaluate the complexity of short-term HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The experimental results exhibit that MDE is capable of quantifying the decreased complexity of HRV with aging and CHF disease with short-term HRV time series.<\/jats:p>","DOI":"10.3390\/e20120952","type":"journal-article","created":{"date-parts":[[2018,12,12]],"date-time":"2018-12-12T03:27:49Z","timestamp":1544585269000},"page":"952","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Multiscale Distribution Entropy Analysis of Short-Term Heart Rate Variability"],"prefix":"10.3390","volume":"20","author":[{"given":"Dae-Young","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8292-7093","authenticated-orcid":false,"given":"Young-Seok","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.bspc.2018.03.003","article-title":"A survey on ECG analysis","volume":"43","author":"Uysal","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1016\/j.compbiomed.2007.01.012","article-title":"Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure","volume":"37","author":"Kuntalp","year":"2007","journal-title":"Comput. Biol. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1007\/s11517-006-0119-0","article-title":"Heart rate variability: A review","volume":"44","author":"Kannathal","year":"2006","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1016\/j.compbiomed.2012.06.005","article-title":"Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability","volume":"42","author":"Yu","year":"2012","journal-title":"Comput. Biol. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"068102","DOI":"10.1103\/PhysRevLett.89.068102","article-title":"Multiscale Entropy Analysis of Complex Physiologic Time Series","volume":"89","author":"Costa","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S0197-4580(01)00266-4","article-title":"What is physiologic complexity and how does it change with aging and disease?","volume":"23","author":"Goldberger","year":"2002","journal-title":"Neurobiol. Aging"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Villecco, F., and Pellegrino, A. (2017). Entropic Measure of Epistemic Uncertainties in Multibody System Models by Axiomatic Design. Entropy, 19.","DOI":"10.3390\/e19070291"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Villecco, F., and Pellegrino, A. (2017). Evaluation of Uncertainties in the Design Process of Complex Mechanical Systems. Entropy, 19.","DOI":"10.3390\/e19090475"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sena, P., Attianese, P., Pappalardo, M., and Villecco, F. (2012, January 8\u201310). FIDELITY: Fuzzy Inferential Diagnostic Engine for on-LIne supporT to phYsicians. Proceedings of the 4th International Conference on the Development of Biomedical Engineering, Ho Chi Minh City, Vietnam. IFMBE Proceedings.","DOI":"10.1007\/978-3-642-32183-2_95"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1109\/18.119732","article-title":"Entropy-based algorithms for best basis selection","volume":"38","author":"Coifman","year":"1992","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/TNSRE.2007.897025","article-title":"Characterization of Surface EMG Signal Based on Fuzzy Entropy","volume":"15","author":"Chen","year":"2007","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation Entropy: A Natural Complexity Measure for Time Series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.1109\/TBME.2006.889772","article-title":"Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome","volume":"54","author":"Sahakian","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.compbiomed.2012.11.005","article-title":"Analysis of heart rate variability using fuzzy measure entropy","volume":"43","author":"Liu","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shi, B., Zhang, Y., Yuan, C., Wang, S., and Li, P. (2017). Entropy Analysis of Short-Term Heartbeat Interval Time Series during Regular Walking. Entropy, 19.","DOI":"10.3390\/e19100568"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"231","DOI":"10.3390\/e17010231","article-title":"Multiscale Entropy Analysis of Heart Rate Variability for Assessing the Severity of Sleep Disordered Breathing","volume":"17","author":"Pan","year":"2015","journal-title":"Entropy"},{"key":"ref_18","first-page":"971","article-title":"Complexity and 1\/f noise. A phase space approach","volume":"1","author":"Zhang","year":"1991","journal-title":"J. Phys. I"},{"key":"ref_19","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"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"18003","DOI":"10.1209\/0295-5075\/107\/18003","article-title":"Mood states modulate complexity in heartbeat dynamics: A multiscale entropy analysis","volume":"107","author":"Valenza","year":"2014","journal-title":"EPL"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Watanabe, E., Kiyono, K., Hayano, J., Yamamoto, Y., Inamasu, J., Yamamoto, M., Ichikawa, T., Sobue, Y., Harada, M., and Ozaki, Y. (2015). Multiscale Entropy of the Heart Rate Variability for the Prediction of an Ischemic Stroke in Patients with Permanent Atrial Fibrillation. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0137144"},{"key":"ref_22","first-page":"145","article-title":"Multiscale fuzzy entropy and its application in rolling bearing fault diagnosis","volume":"27","author":"Jinde","year":"2014","journal-title":"Zhendong Gongcheng Xuebao\/J. Vib. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.eplepsyres.2012.11.003","article-title":"Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis","volume":"104","author":"Ouyang","year":"2013","journal-title":"Epilepsy Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5865","DOI":"10.1016\/j.physa.2013.07.075","article-title":"Modified multiscale entropy for short-term time series analysis","volume":"392","author":"Wu","year":"2013","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s11517-014-1216-0","article-title":"Assessing the complexity of short-term heartbeat interval series by distribution entropy","volume":"53","author":"Li","year":"2015","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_26","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":"PNAS"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A Real-Time QRS Detection Algorithm","volume":"BME-32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/12\/952\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:33:09Z","timestamp":1760196789000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/12\/952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,11]]},"references-count":28,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["e20120952"],"URL":"https:\/\/doi.org\/10.3390\/e20120952","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,11]]}}}