{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T05:22:12Z","timestamp":1775020932536,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T00:00:00Z","timestamp":1684800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["CEECIND\/03986\/2018"],"award-info":[{"award-number":["CEECIND\/03986\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["CEECINST\/00013\/2021"],"award-info":[{"award-number":["CEECINST\/00013\/2021"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["CEECINST\/00123\/2021"],"award-info":[{"award-number":["CEECINST\/00123\/2021"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/00127\/2020"],"award-info":[{"award-number":["UIDB\/00127\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/04293\/2020"],"award-info":[{"award-number":["UIDB\/04293\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["LA\/P\/0063\/2020"],"award-info":[{"award-number":["LA\/P\/0063\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Multiple studies show an association between anxiety disorders and dysregulation in the Autonomic Nervous System (ANS). Thus, understanding how informative the physiological signals are would contribute to effectively detecting anxiety. This study targets the classification of anxiety as an imbalanced binary classification problem using physiological signals collected from a sample of healthy subjects under a neutral condition. For this purpose, the Electrocardiogram (ECG), Electrodermal Activity (EDA), and Electromyogram (EMG) signals from the WESAD publicly available dataset were used. The neutral condition was collected for around 20 min on 15 participants, and anxiety scores were assessed through the shortened 6-item STAI. To achieve the described goal, the subsequent steps were followed: signal pre-processing; feature extraction, analysis, and selection; and classification of anxiety. The findings of this study allowed us to classify anxiety with discriminatory class features based on physiological signals. Moreover, feature selection revealed that ECG features play a relevant role in anxiety classification. Supervised feature selection and data balancing techniques, especially Borderline SMOTE 2, increased the performance of most classifiers. In particular, the combination of feature selection and Borderline SMOTE 2 achieved the best ROC-AUC with the Random Forest classifier.<\/jats:p>","DOI":"10.3390\/app13116368","type":"journal-article","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T08:14:28Z","timestamp":1684829668000},"page":"6368","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Multimodal Classification of Anxiety Based on Physiological Signals"],"prefix":"10.3390","volume":"13","author":[{"given":"Mariana","family":"Vaz","sequence":"first","affiliation":[{"name":"Computer Science Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2548-6281","authenticated-orcid":false,"given":"Teresa","family":"Summavielle","sequence":"additional","affiliation":[{"name":"Addiction Biology Group, i3S\u2014Instituto de Investiga\u00e7\u00e3o e Inova\u00e7\u00e3o em Sa\u00fade, 4200-135 Porto, Portugal"},{"name":"Escola Superior de Sa\u00fade, Polytechnic of Porto, 4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5717-1415","authenticated-orcid":false,"given":"Raquel","family":"Sebasti\u00e3o","sequence":"additional","affiliation":[{"name":"Institute of Electronics and Informatics Engineering of Aveiro (IEETA), Department of Electronics, Telecommunications and Informatics (DETI), Intelligent Systems Associate Laboratory (LASI), University of Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6852-8077","authenticated-orcid":false,"given":"Rita P.","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"},{"name":"INESC TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Reiss, A., Duerichen, R., and Van Laerhoven, K. 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