{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T02:04:31Z","timestamp":1785377071371,"version":"3.55.0"},"reference-count":34,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,2,11]],"date-time":"2024-02-11T00:00:00Z","timestamp":1707609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Dioni: Computing Infrastructure for Big-Data Processing and Analysis","award":["5047222"],"award-info":[{"award-number":["5047222"]}]},{"name":"Dioni: Computing Infrastructure for Big-Data Processing and Analysis","award":["NSRF 2014\u20132020"],"award-info":[{"award-number":["NSRF 2014\u20132020"]}]},{"name":"Operational Programme \u201cCompetitiveness, Entrepreneurship and Innovation\u201d","award":["5047222"],"award-info":[{"award-number":["5047222"]}]},{"name":"Operational Programme \u201cCompetitiveness, Entrepreneurship and Innovation\u201d","award":["NSRF 2014\u20132020"],"award-info":[{"award-number":["NSRF 2014\u20132020"]}]},{"name":"Greece and the European Union (European Regional Development Fund)","award":["5047222"],"award-info":[{"award-number":["5047222"]}]},{"name":"Greece and the European Union (European Regional Development Fund)","award":["NSRF 2014\u20132020"],"award-info":[{"award-number":["NSRF 2014\u20132020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>The objective of this study was to evaluate the effectiveness of machine learning classification techniques applied to nerve conduction studies (NCS) of motor and sensory signals for the automatic diagnosis of carpal tunnel syndrome (CTS). Two methodologies were tested. In the first methodology, motor signals recorded from the patients\u2019 median nerve were transformed into time-frequency spectrograms using the short-time Fourier transform (STFT). These spectrograms were then used as input to a deep two-dimensional convolutional neural network (CONV2D) for classification into two categories: patients and controls. In the second methodology, sensory signals from the patients\u2019 median and ulnar nerves were subjected to multilevel wavelet decomposition (MWD), and statistical and non-statistical features were extracted from the decomposed signals. These features were utilized to train and test classifiers. The classification target was set to three categories: normal subjects (controls), patients with mild CTS, and patients with moderate to severe CTS based on conventional electrodiagnosis results. The results of the classification analysis demonstrated that both methodologies surpassed previous attempts at automatic CTS diagnosis. The classification models utilizing the motor signals transformed into time-frequency spectrograms exhibited excellent performance, with average accuracy of 94%. Similarly, the classifiers based on the sensory signals and the extracted features from multilevel wavelet decomposition showed significant accuracy in distinguishing between controls, patients with mild CTS, and patients with moderate to severe CTS, with accuracy of 97.1%. The findings highlight the efficacy of incorporating machine learning algorithms into the diagnostic processes of NCS, providing a valuable tool for clinicians in the diagnosis and management of neuropathies such as CTS.<\/jats:p>","DOI":"10.3390\/bioengineering11020175","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T03:50:27Z","timestamp":1707709827000},"page":"175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Carpal Tunnel Syndrome Automated Diagnosis: A Motor vs. Sensory Nerve Conduction-Based Approach"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8448-2120","authenticated-orcid":false,"given":"Dimitrios","family":"Bakalis","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prokopis","family":"Kontogiannis","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6739-6108","authenticated-orcid":false,"given":"Evangelos","family":"Ntais","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Hospital of Ioannina, 45110 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1764-8906","authenticated-orcid":false,"given":"Yannis V.","family":"Simos","sequence":"additional","affiliation":[{"name":"Department of Physiology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7032-1630","authenticated-orcid":false,"given":"Konstantinos I.","family":"Tsamis","sequence":"additional","affiliation":[{"name":"Department of Neurology, University Hospital of Ioannina, 45110 Ioannina, Greece"},{"name":"Department of Physiology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7044-860X","authenticated-orcid":false,"given":"George","family":"Manis","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, School of Engineering, University of Ioannina, 45110 Ioannina, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pimentel, B.F.R., Faloppa, F., Tamaoki, M.J.S., and Belloti, J.C. 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