{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T16:03:34Z","timestamp":1780502614756,"version":"3.54.1"},"reference-count":45,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T00:00:00Z","timestamp":1672358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Slope Entropy (SlpEn) is a very recently proposed entropy calculation method. It is based on the differences between consecutive values in a time series and two new input thresholds to assign a symbol to each resulting difference interval. As the histogram normalisation value, SlpEn uses the actual number of unique patterns found instead of the theoretically expected value. This maximises the information captured by the method but, as a consequence, SlpEn results do not usually fall within the classical [0,1] interval. Although this interval is not necessary at all for time series classification purposes, it is a convenient and common reference framework when entropy analyses take place. This paper describes a method to keep SlpEn results within this interval, and improves the interpretability and comparability of this measure in a similar way as for other methods. It is based on a max\u2013min normalisation scheme, described in two steps. First, an analytic normalisation is proposed using known but very conservative bounds. Afterwards, these bounds are refined using heuristics about the behaviour of the number of patterns found in deterministic and random time series. The results confirm the suitability of the approach proposed, using a mixture of the two methods.<\/jats:p>","DOI":"10.3390\/e25010066","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T03:31:17Z","timestamp":1672371077000},"page":"66","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Slope Entropy Normalisation by Means of Analytical and Heuristic Reference Values"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0076-0515","authenticated-orcid":false,"given":"David","family":"Cuesta-Frau","sequence":"first","affiliation":[{"name":"Technological Institute of Informatics (ITI), Universitat Polit\u00e8cnica de Val\u00e8ncia, Alcoi Campus, 03801 Alcoi, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdy","family":"Kouka","sequence":"additional","affiliation":[{"name":"Department of System Informatics and Computers, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7091-0040","authenticated-orcid":false,"given":"Javier","family":"Silvestre-Blanes","sequence":"additional","affiliation":[{"name":"Technological Institute of Informatics (ITI), Universitat Polit\u00e8cnica de Val\u00e8ncia, Alcoi Campus, 03801 Alcoi, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9271-2010","authenticated-orcid":false,"given":"V\u00edctor","family":"Sempere-Pay\u00e1","sequence":"additional","affiliation":[{"name":"Technological Institute of Informatics (ITI), Universitat Polit\u00e8cnica de Val\u00e8ncia, Alcoi Campus, 03801 Alcoi, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,30]]},"reference":[{"key":"ref_1","first-page":"419","article-title":"What Can Biosignal Entropy Tell Us About Health and Disease? 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