Szczegóły publikacji
Opis bibliograficzny
Analysis of voice, speech, and language biomarkers of Parkinson’s disease collected in a mixed reality setting / Miłosz DUDEK, Daria HEMMERLING, Marta Kaczmarska, Joanna Stępień, Mateusz DANIOŁ, Marek WODZIŃSKI, Magdalena Wójcik-Pędziwiatr // Sensors [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1424-8220. — 2025 — vol. 25 iss. 8 art. no. 2405, s. 1–29. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 26–29, Abstr. — Publikacja dostępna online od: 2025-04-10
Autorzy (7)
- AGHDudek Miłosz
- AGHHemmerling Daria
- AGHKaczmarska Marta
- AGHStępień Joanna
- AGHDanioł Mateusz
- AGHWodziński Marek
- Wójcik-Pędziwiatr Magdalena
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 159477 |
|---|---|
| Data dodania do BaDAP | 2025-04-28 |
| Tekst źródłowy | URL |
| DOI | 10.3390/s25082405 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Sensors |
Abstract
This study explores an innovative approach to early Parkinson’s disease (PD) detection by analyzing speech data collected using a mixed reality (MR) system. A total of 57 Polish participants, including PD patients and healthy controls, performed five speech tasks while using an MR head-mounted display (HMD). Speech data were recorded and analyzed to extract acoustic and linguistic features, which were then evaluated using machine learning models, including logistic regression, support vector machines (SVMs), random forests, AdaBoost, and XGBoost. The XGBoost model achieved the best performance, with an F1-score of 0.90 ± 0.05 in the story-retelling task. Key features such as MFCCs (mel-frequency cepstral coefficients), spectral characteristics, RASTA-filtered auditory spectrum, and local shimmer were identified as significant in detecting PD-related speech alterations. Additionally, state-of-the-art deep learning models (wav2vec2, HuBERT, and WavLM) were fine-tuned for PD detection. HuBERT achieved the highest performance, with an F1-score of 0.94 ± 0.04 in the diadochokinetic task, demonstrating the potential of deep learning to capture complex speech patterns linked to neurodegenerative diseases. This study highlights the effectiveness of combining MR technology for speech data collection with advanced machine learning (ML) and deep learning (DL) techniques, offering a non-invasive and high-precision approach to PD diagnosis. The findings hold promise for broader clinical applications, advancing the diagnostic landscape for neurodegenerative disorders.
