{"title":"近期(2015-2024)应用于生理信号的熵定义综述","authors":"Dimitrios Platakis, George Manis","doi":"10.3390/e27090983","DOIUrl":null,"url":null,"abstract":"<p><p>Entropy estimation is widely used in time series analysis, particularly in the field of Biomedical Engineering. It plays a key role in analyzing a wide range of physiological signals and serves as a measure of signal complexity, which reflects the complexity of the underlying system. The widespread adoption of entropy in research has led to numerous entropy definitions, with Approximate Entropy and Sample Entropy being among the most widely used. Over the past decade, the field has remained highly active, with a significant number of new entropy definitions being proposed, some inspired by Approximate and Sample Entropy, some by Permutation entropy, while others followed their own course of thought. In this paper, we review and compare the most prominent entropy definitions that have appeared in the last decade (2015-2024). We performed the search on 20 December 2024. We adopt the PRISMA methodology for this purpose, a widely accepted standard for conducting systematic literature reviews. With the included articles, we present statistical results on the number of citations for each method and the application domains in which they have been used. We also conducted a thorough review of the selected articles, documenting for each paper which definition has been employed and on which physiological signal it has been applied.</p>","PeriodicalId":11694,"journal":{"name":"Entropy","volume":"27 9","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468477/pdf/","citationCount":"0","resultStr":"{\"title\":\"Review of Recent (2015-2024) Popular Entropy Definitions Applied to Physiological Signals.\",\"authors\":\"Dimitrios Platakis, George Manis\",\"doi\":\"10.3390/e27090983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Entropy estimation is widely used in time series analysis, particularly in the field of Biomedical Engineering. It plays a key role in analyzing a wide range of physiological signals and serves as a measure of signal complexity, which reflects the complexity of the underlying system. The widespread adoption of entropy in research has led to numerous entropy definitions, with Approximate Entropy and Sample Entropy being among the most widely used. Over the past decade, the field has remained highly active, with a significant number of new entropy definitions being proposed, some inspired by Approximate and Sample Entropy, some by Permutation entropy, while others followed their own course of thought. In this paper, we review and compare the most prominent entropy definitions that have appeared in the last decade (2015-2024). We performed the search on 20 December 2024. We adopt the PRISMA methodology for this purpose, a widely accepted standard for conducting systematic literature reviews. With the included articles, we present statistical results on the number of citations for each method and the application domains in which they have been used. We also conducted a thorough review of the selected articles, documenting for each paper which definition has been employed and on which physiological signal it has been applied.</p>\",\"PeriodicalId\":11694,\"journal\":{\"name\":\"Entropy\",\"volume\":\"27 9\",\"pages\":\"\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468477/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Entropy\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.3390/e27090983\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Entropy","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.3390/e27090983","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
Review of Recent (2015-2024) Popular Entropy Definitions Applied to Physiological Signals.
Entropy estimation is widely used in time series analysis, particularly in the field of Biomedical Engineering. It plays a key role in analyzing a wide range of physiological signals and serves as a measure of signal complexity, which reflects the complexity of the underlying system. The widespread adoption of entropy in research has led to numerous entropy definitions, with Approximate Entropy and Sample Entropy being among the most widely used. Over the past decade, the field has remained highly active, with a significant number of new entropy definitions being proposed, some inspired by Approximate and Sample Entropy, some by Permutation entropy, while others followed their own course of thought. In this paper, we review and compare the most prominent entropy definitions that have appeared in the last decade (2015-2024). We performed the search on 20 December 2024. We adopt the PRISMA methodology for this purpose, a widely accepted standard for conducting systematic literature reviews. With the included articles, we present statistical results on the number of citations for each method and the application domains in which they have been used. We also conducted a thorough review of the selected articles, documenting for each paper which definition has been employed and on which physiological signal it has been applied.
期刊介绍:
Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.