{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:49:18Z","timestamp":1760233758862,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T00:00:00Z","timestamp":1613779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/N509644\/1 2107746"],"award-info":[{"award-number":["EP\/N509644\/1 2107746"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/R004498\/1"],"award-info":[{"award-number":["MR\/R004498\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Network physiology has emerged as a promising paradigm for the extraction of clinically relevant information from physiological signals by moving from univariate to multivariate analysis, allowing for the inspection of interdependencies between organ systems. However, for its successful implementation, the disruptive effects of artifactual outliers, which are a common occurrence in physiological recordings, have to be studied, quantified, and addressed. Within the scope of this study, we utilize Dispersion Entropy (DisEn) to initially quantify the capacity of outlier samples to disrupt the values of univariate and multivariate features extracted with DisEn from physiological network segments consisting of synchronised, electroencephalogram, nasal respiratory, blood pressure, and electrocardiogram signals. The DisEn algorithm is selected due to its efficient computation and good performance in the detection of changes in signals for both univariate and multivariate time-series. The extracted features are then utilised for the training and testing of a logistic regression classifier in univariate and multivariate configurations in an effort to partially automate the detection of artifactual network segments. Our results indicate that outlier samples cause significant disruption in the values of extracted features with multivariate features displaying a certain level of robustness based on the number of signals formulating the network segments from which they are extracted. Furthermore, the deployed classifiers achieve noteworthy performance, where the percentage of correct network segment classification surpasses 95% in a number of experimental setups, with the effectiveness of each configuration being affected by the signal in which outliers are located. Finally, due to the increase in the number of features extracted within the framework of network physiology and the observed impact of artifactual samples in the accuracy of their values, the implementation of algorithmic steps capable of effective feature selection is highlighted as an important area for future research.<\/jats:p>","DOI":"10.3390\/e23020244","type":"journal-article","created":{"date-parts":[[2021,2,21]],"date-time":"2021-02-21T21:15:01Z","timestamp":1613942101000},"page":"244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Assessment of Outliers and Detection of Artifactual Network Segments Using Univariate and Multivariate Dispersion Entropy on Physiological Signals"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9599-7656","authenticated-orcid":false,"given":"Evangelos","family":"Kafantaris","sequence":"first","affiliation":[{"name":"School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh EH9 3FB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1434-6347","authenticated-orcid":false,"given":"Ian","family":"Piper","sequence":"additional","affiliation":[{"name":"Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh EH16 4UX, UK"},{"name":"Royal Hospital for Sick Children, NHS Lothian, Edinburgh EH9 1LF, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0701-6534","authenticated-orcid":false,"given":"Tsz-Yan Milly","family":"Lo","sequence":"additional","affiliation":[{"name":"Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh EH16 4UX, UK"},{"name":"Royal Hospital for Sick Children, NHS Lothian, Edinburgh EH9 1LF, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2105-8725","authenticated-orcid":false,"given":"Javier","family":"Escudero","sequence":"additional","affiliation":[{"name":"School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh EH9 3FB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bashan, A., Bartsch, R.P., Kantelhardt, J.W., Havlin, S., and Ivanov, P.C. 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