Szczegóły publikacji
Opis bibliograficzny
Vision transformer for Parkinson’s disease classification using multilingual sustained vowel recordings / Daria HEMMERLING, Marek WODZIŃSKI, Juan Rafael Orozco-Arroyave, David Sztaho, Mateusz DANIOŁ, Paweł JEMIOŁO, Magdalena Wójcik-Pędziwiatr // W: EMBC 2023 [Dokument elektroniczny] : 2023 45th annual international conference of the IEEE Engineering in Medicine & Biology Conference : Sydney, Australia, 24-27 July 2023 : proceedings. — Wersja do Windows. — Dane tekstowe. — [Piscataway, NJ, USA] : IEEE, cop. 2023. — (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; ISSN 1094-687X). — Dod. ISBN 979-8-3503-2448-8 (PoD). — e-ISBN: 979-8-3503-2447-1. — S. [1-4]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [4], Abstr. — M. Wodziński - dod. afiliacja: University of Applied Sciences Western Switzerland
Autorzy (7)
- AGHHemmerling Daria
- AGHWodziński Marek
- Orozco-Arroyave Juan Rafael
- Sztaho David
- AGHDanioł Mateusz
- AGHJemioło Paweł
- Wójcik-Pędziwiatr Magdalena
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150883 |
|---|---|
| Data dodania do BaDAP | 2024-01-29 |
| Tekst źródłowy | URL |
| DOI | 10.1109/EMBC40787.2023.10340478 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | The Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2023 |
| Czasopismo/seria | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Abstract
Parkinson’s disease (PD) is the 2 nd most prevalent neurodegenerative disease in the world. Thus, the early detection of PD has recently been the subject of several scientific and commercial studies. In this paper, we propose a pipeline using Vision Transformer applied to mel-spectrograms for PD classification using multilingual sustained vowel recordings. Furthermore, our proposed transformed-based model shows a great potential to use voice as a single modality biomarker for automatic PD detection without language restrictions, a wide range of vowels, with an F1-score equal to 0.78. The results of our study fall within the range of the estimated prevalence of voice and speech disorders in Parkinson’s disease, which ranges from 70-90%. Our study demonstrates a high potential for adaptation in clinical decision-making, allowing for increasingly systematic and fast diagnosis of PD with the potential for use in telemedicine.Clinical relevance— There is an urgent need to develop non invasive biomarker of Parkinson’s disease effective enough to detect the onset of the disease to introduce neuroprotective treatment at the earliest stage possible and to follow the results of that intervention. Voice disorders in PD are very frequent and are expected to be utilized as an early diagnostic biomarker. The voice analysis using deep neural networks open new opportunities to assess neurodegenerative diseases’ symptoms, for fast diagnosis-making, to guide treatment initiation, and risk prediction. The detection accuracy for voice biomarkers according to our method reached close to the maximum achievable value.
