{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T08:11:46Z","timestamp":1783411906516,"version":"3.54.6"},"reference-count":68,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T00:00:00Z","timestamp":1682467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Parkinson\u2019s disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis. It is defined by hypokinetic dysarthria and accounts for respiratory, phonatory, articulatory, and prosodic manifestations. The topic of this article targets artificial-intelligence-based identification of Parkinson\u2019s disease from continuous speech recorded in a noisy environment. The novelty of this work is twofold. First, the proposed assessment workflow performed speech analysis on samples of continuous speech. Second, we analyzed and quantified Wiener filter applicability for speech denoising in the context of Parkinsonian speech identification. We argue that the Parkinsonian features of loudness, intonation, phonation, prosody, and articulation are contained in the speech, speech energy, and Mel spectrograms. Thus, the proposed workflow follows a feature-based speech assessment to determine the feature variation ranges, followed by speech classification using convolutional neural networks. We report the best classification accuracies of 96% on speech energy, 93% on speech, and 92% on Mel spectrograms. We conclude that the Wiener filter improves both feature-based analysis and convolutional-neural-network-based classification performances.<\/jats:p>","DOI":"10.3390\/bioengineering10050531","type":"journal-article","created":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T02:18:34Z","timestamp":1682561914000},"page":"531","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["CNN-Based Identification of Parkinson\u2019s Disease from Continuous Speech in Noisy Environments"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7824-8506","authenticated-orcid":false,"given":"Paul","family":"Farag\u00f3","sequence":"first","affiliation":[{"name":"Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6211-9205","authenticated-orcid":false,"given":"Sebastian-Aurelian","family":"\u0218tef\u0103nig\u0103","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Mathematics and Computer Science, West University of Timisoara, 300223 Timisoara, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudia-Georgiana","family":"Cordo\u0219","sequence":"additional","affiliation":[{"name":"Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura-Ioana","family":"Mih\u0103il\u0103","sequence":"additional","affiliation":[{"name":"Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sorin","family":"Hintea","sequence":"additional","affiliation":[{"name":"Bases of Electronics Department, Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana-Sorina","family":"Pe\u0219tean","sequence":"additional","affiliation":[{"name":"Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy \u201cIuliu Hatieganu\u201d Cluj-Napoca, 400012 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michel","family":"Beyer","sequence":"additional","affiliation":[{"name":"Clinic of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, CH-4031 Basel, Switzerland"},{"name":"Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, CH-4123 Allschwil, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L\u0103cr\u0103mioara","family":"Perju-Dumbrav\u0103","sequence":"additional","affiliation":[{"name":"Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy \u201cIuliu Hatieganu\u201d Cluj-Napoca, 400012 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4964-2285","authenticated-orcid":false,"given":"Robert Radu","family":"Ile\u0219an","sequence":"additional","affiliation":[{"name":"Department of Neurology and Pediatric Neurology, Faculty of Medicine, University of Medicine and Pharmacy \u201cIuliu Hatieganu\u201d Cluj-Napoca, 400012 Cluj-Napoca, Romania"},{"name":"Clinic of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, CH-4031 Basel, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,26]]},"reference":[{"key":"ref_1","unstructured":"Triarhou, L.C. 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