Speech features for telemonitoring of Parkinson's disease symptoms.
Akan, Ozgur B
Annu Int Conf IEEE Eng Med Biol Soc
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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Ramezani, H., Khaki, H., Erzin, E., & Akan, O. B. (2017). Speech features for telemonitoring of Parkinson's disease symptoms.. Annu Int Conf IEEE Eng Med Biol Soc, 2017 3801-3805. https://doi.org/10.1109/EMBC.2017.8037685
The aim of this paper is tracking Parkinson's disease (PD) progression based on its symptoms on vocal system using Unified Parkinsons Disease Rating Scale (UPDRS). We utilize a standard speech signal feature set, which contains 6373 static features as functionals of low-level descriptor (LLD) contours, and select the most informative ones using the maximal relevance and minimal redundancy based on correlations (mRMRC) criteria. Then, we evaluate performance of Gaussian mixture regression (GMR) and support vector regression (SVR) on estimating the third subscale of UPDRS, i.e., UPDRS: motor subscale (UPDRS-III). Among the most informative features, a list of features are selected after redundancy reduction. The selected features depict that LLDs providing information about spectrum flatness, spectral distribution of energy, and hoarseness of voice are the most important ones for estimating UPDRS-III. Moreover, the most informative statistical functions are related to range, maximum, minimum and standard deviation of LLDs, which is an evidence of the muscle weakness due to the PD. Furthermore, GMR outperforms SVR on compact feature sets while the performance of SVR improves by increasing number of features.
Humans, Parkinson Disease, Disease Progression, Severity of Illness Index, Speech, Voice
European Research Council (616922)
European Commission Horizon 2020 (H2020) Future and Emerging Technologies (FET) (665564)
External DOI: https://doi.org/10.1109/EMBC.2017.8037685
This record's URL: https://www.repository.cam.ac.uk/handle/1810/287029