{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T10:14:12Z","timestamp":1768817652281,"version":"3.49.0"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,2,26]],"date-time":"2018-02-26T00:00:00Z","timestamp":1519603200000},"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>This study\u2019s aim was to apply permutation entropy (PE) and permutation min-entropy (PME) over an RR interval time series to quantify the changes in cardiac activity among multiple emotional states. Electrocardiogram (ECG) signals were recorded under six emotional states (neutral, happiness, sadness, anger, fear, and disgust) in 60 healthy subjects at a rate of 1000 Hz. For each emotional state, ECGs were recorded for 5 min and the RR interval time series was extracted from these ECGs. The obtained results confirm that PE and PME increase significantly during the emotional states of happiness, sadness, anger, and disgust. Both symbolic quantifiers also increase but not in a significant way for the emotional state of fear. Moreover, it is found that PME is more sensitive than PE for discriminating non-neutral from neutral emotional states.<\/jats:p>","DOI":"10.3390\/e20030148","type":"journal-article","created":{"date-parts":[[2018,2,27]],"date-time":"2018-02-27T03:36:12Z","timestamp":1519702572000},"page":"148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series"],"prefix":"10.3390","volume":"20","author":[{"given":"Yirong","family":"Xia","sequence":"first","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan, 250061, China"}]},{"given":"Licai","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan, 250061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2852-3263","authenticated-orcid":false,"given":"Luciano","family":"Zunino","sequence":"additional","affiliation":[{"name":"Centro de Investigaciones \u00d3pticas (CONICET La Plata\u2014CIC), C.C. 3, 1897 Gonnet, Argentina"},{"name":"Departamento de Ciencias B\u00e1sicas, Facultad de Ingenier\u00eda, Universidad Nacional de La Plata (UNLP), 1900 La Plata, Argentina"}]},{"given":"Hongyu","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan, 250061, China"}]},{"given":"Yuan","family":"Zhuang","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan, 250061, China"}]},{"given":"Chengyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing 210018, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.compeleceng.2016.04.009","article-title":"A novel feature extraction method based on late positive potential for emotion recognition in human brain signal patterns","volume":"53","author":"Mehmood","year":"2016","journal-title":"Comput. 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