{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T07:35:25Z","timestamp":1776065725012,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T00:00:00Z","timestamp":1666915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomedicines"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer. We propose the application of a modified SqueezeNet convolutional neural network (CNN) for detecting PD based on the subject\u2019s key-typing patterns. First, the data are pre-processed using data standardization and the Synthetic Minority Oversampling Technique (SMOTE), and then a Continuous Wavelet Transformation is applied to generate spectrograms used for training and testing a modified SqueezeNet model. The modified SqueezeNet model achieved an accuracy of 90%, representing a noticeable improvement in comparison to other approaches.<\/jats:p>","DOI":"10.3390\/biomedicines10112746","type":"journal-article","created":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T23:45:00Z","timestamp":1667087100000},"page":"2746","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Modified SqueezeNet Architecture for Parkinson\u2019s Disease Detection Based on Keypress Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Lucas Salvador","family":"Bernardo","sequence":"first","affiliation":[{"name":"Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0849-5098","authenticated-orcid":false,"given":"Sai Ho","family":"Ling","sequence":"additional","affiliation":[{"name":"Department of Electrical and Data Engineering, University of Technology Sydney, Sydney 2007, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3886-4309","authenticated-orcid":false,"given":"Victor Hugo C.","family":"de Albuquerque","sequence":"additional","affiliation":[{"name":"Department of Teleinformatics Engineering, Federal University of Cear\u00e1, Fortaleza 60455-970, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7603-6526","authenticated-orcid":false,"given":"Jo\u00e3o Manuel R. S.","family":"Tavares","sequence":"additional","affiliation":[{"name":"Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial, Departamento de Engenharia Mec\u00e2nica, Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.future.2018.02.009","article-title":"Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease","volume":"83","author":"Abdulhay","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/s41572-021-00280-3","article-title":"Parkinson disease-associated cognitive impairment","volume":"7","author":"Aarsland","year":"2021","journal-title":"Nat. Rev. Dis. 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