ParkinsoNET: Estimation of UPDRS Score Using Hubness-Aware Feedforward Neural Networks
| Autor: | Krisztian Buza, Noémi Ágnes Varga |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Estimation
business.industry Computer science 0206 medical engineering Context (language use) 02 engineering and technology computer.software_genre Machine learning 020601 biomedical engineering 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Rating scale Feedforward neural network Data mining Artificial intelligence business Error detection and correction computer 030217 neurology & neurosurgery |
| Zdroj: | Applied Artificial Intelligence. 30:541-555 |
| ISSN: | 1087-6545 0883-9514 |
| DOI: | 10.1080/08839514.2016.1193716 |
| Popis: | Parkinson’s disease is a worldwide, frequent, neurodegenerative disorder with increasing incidence. Speech disturbance appears during the progression of the disease. The Unified Parkinson’s Disease Rating Scale UPDRS is a gold-standard tool for diagnosis and follow-up of the disease. We aim at estimating the UPDRS score based on biomedical voice recordings. In this article, we study the hubness phenomenon in context of the UPDRS score estimation and propose hubness-aware error correction for feedforward neural networks to increase the accuracy of estimation. We perform experiments on publicly available datasets derived from real-voice data and show that the proposed technique systematically increases the accuracy of various feedforward neural networks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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