ParkinsoNET: Estimation of UPDRS Score Using Hubness-Aware Feedforward Neural Networks

ABSTRACT

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.

Funding

This research was performed within the framework of the grant of the Hungarian Scientific Research Fund – OTKA PD 111710. This article was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. N. Á. Varga was supported by the KTIA NAP 13 1-2013-0001.

Notes

1. http://www.pdf.org/en/parkinson_statistics

2. http://archive.ics.uci.edu/ml/datasets/Parkinsons+Telemonitoring

Additional information

Funding

This research was performed within the framework of the grant of the Hungarian Scientific Research Fund – OTKA PD 111710. This article was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. N. Á. Varga was supported by the KTIA NAP 13 1-2013-0001.