{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T10:00:30Z","timestamp":1790848830052,"version":"4.1.0"},"reference-count":29,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T00:00:00Z","timestamp":1599091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vibroarthrography (VAG) is a non-invasive and potentially widely available method supporting the joint diagnosis process. This research was conducted using VAG signals classified to five different condition classes: three stages of chondromalacia patellae, osteoarthritis, and control group (healthy knee joint). Ten new spectral features were proposed, distinguishing not only neighboring classes, but every class combination. Additionally, Frequency Range Maps were proposed as the frequency feature extraction visualization method. The results were compared to state-of-the-art frequency features using the Bhattacharyya coefficient and the set of ten different classification algorithms. All methods evaluating proposed features indicated the superiority of the new features compared to the state-of-the-art. In terms of Bhattacharyya coefficient, newly proposed features proved to be over 25% better, and the classification accuracy was on average 9% better.<\/jats:p>","DOI":"10.3390\/s20175015","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T11:24:24Z","timestamp":1599218664000},"page":"5015","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9562-7345","authenticated-orcid":false,"given":"Adam","family":"\u0141ysiak","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Fro\u0144","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawid","family":"B\u0105czkowicz","sequence":"additional","affiliation":[{"name":"Faculty of Physical Education and Physiotherapy, Opole University of Technology, 45-758 Opole, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3467-951X","authenticated-orcid":false,"given":"Miros\u0142aw","family":"Szmajda","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.cmpb.2017.10.027","article-title":"Analysis and multiclass classification of pathological knee joints using vibroarthrographic signals","volume":"154","year":"2018","journal-title":"Comput. 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