Abstract
Tracking Parkinson’s disease symptom severity by using characteristics automatically extracted from voice recordings is a very interesting and challenging problem. In this context, voice features are automatically extracted from multiple voice recordings from the same subjects. In principle, for each subject, the features should be identical at a concrete time, but the imperfections in technology and the own biological variability result in nonidentical replicated features. The involved within-subject variability must be addressed since replicated measurements from voice recordings can not be directly used in independence-based pattern recognition methods as they have been routinely used through the scientific literature. Besides, the time plays a key role in the experimental design. In this paper, for the first time, a Bayesian linear regression approach suitable to handle replicated measurements and time is proposed. Moreover, a version favoring the best predictors and penalizing the worst ones is also presented. Computational difficulties have been avoided by developing Gibbs sampling-based approaches.
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Notes
http://www.parkinsonsvoice.org/.
https://archive.ics.uci.edu/ml/datasets/Parkinsons+Telemonitoring.
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Acknowledgments
Thanks to Athanasios Tsanas for providing information on the dataset. We thank four reviewers for comments and suggestions which have highly improved this paper. This research has been partially supported by Ministerio de Economía y Competitividad, Spain (Projects MTM2011-28983-C03-02 and MTM2014-56949-C3-3-R), Gobierno de Extremadura, Spain (Projects GR15052 and GR15106), and European Union (European Regional Development Funds).
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Naranjo, L., Pérez, C.J. & Martín, J. Addressing voice recording replications for tracking Parkinson’s disease progression. Med Biol Eng Comput 55, 365–373 (2017). https://doi.org/10.1007/s11517-016-1512-y
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DOI: https://doi.org/10.1007/s11517-016-1512-y