{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T07:20:40Z","timestamp":1787728840479,"version":"build-2784847793"},"reference-count":106,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Tinnitus is a highly prevalent condition, affecting more than 1 in 7 adults in the EU and causing negative effects on sufferers\u2019 quality of life. In this study, we utilised data collected within the \u201cUNITI\u201d project, the largest EU tinnitus-related research programme. Initially, we extracted characteristics from both auditory brainstem response (ABR) and auditory middle latency response (AMLR) signals, which were derived from tinnitus patients. We then combined these features with the patients\u2019 clinical data, and integrated them to build machine learning models for the classification of individuals and their ears according to their level of tinnitus-related distress. Several models were developed and tested on different datasets to determine the most relevant features and achieve high performances. Specifically, seven widely used classifiers were utilised on all generated datasets: random forest (RF), linear, radial, and polynomial support vector machines (SVM), naive bayes (NB), neural networks (NN), and linear discriminant analysis (LDA). Results showed that features extracted from the wavelet-scattering transformed AMLR signals were the most informative data. In combination with the 15 LASSO-selected clinical features, the SVM classifier achieved optimal performance with an AUC value, sensitivity, and specificity of 92.53%, 84.84%, and 83.04%, respectively, indicating high discrimination performance between the two groups.<\/jats:p>","DOI":"10.3390\/jcm12113843","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T02:18:29Z","timestamp":1685931509000},"page":"3843","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Development of Machine-Learning Models for Tinnitus-Related Distress Classification Using Wavelet-Transformed Auditory Evoked Potential Signals and Clinical Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2071-1144","authenticated-orcid":false,"given":"Ourania","family":"Manta","sequence":"first","affiliation":[{"name":"Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1921-6525","authenticated-orcid":false,"given":"Michail","family":"Sarafidis","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7942-1788","authenticated-orcid":false,"given":"Winfried","family":"Schlee","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany"},{"name":"Institute for Information and Process Management, Eastern Switzerland University of Applied Sciences, 9001 St. Gallen, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9587-6632","authenticated-orcid":false,"given":"Birgit","family":"Mazurek","sequence":"additional","affiliation":[{"name":"Tinnitus Center, Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin, Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin, 10117 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2600-9914","authenticated-orcid":false,"given":"George K.","family":"Matsopoulos","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1205-9918","authenticated-orcid":false,"given":"Dimitrios D.","family":"Koutsouris","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s00106-019-0633-7","article-title":"A Multidisciplinary European Guideline for Tinnitus: Diagnostics, Assessment, and Treatment","volume":"67","author":"Cima","year":"2019","journal-title":"HNO"},{"key":"ref_2","first-page":"278","article-title":"Chronic Tinnitus: An Interdisciplinary Challenge","volume":"110","author":"Kreuzer","year":"2013","journal-title":"Dtsch. 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