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Estimation of Parkinson’s disease severity using speech features and extreme gradient boosting

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Abstract

In recent years, there is an increasing interest in building e-health systems. The systems built to deliver the health services with the use of internet and communication technologies aim to reduce the costs arising from outpatient visits of patients. Some of the related recent studies propose machine learning–based telediagnosis and telemonitoring systems for Parkinson’s disease (PD). Motivated from the studies showing the potential of speech disorders in PD telemonitoring systems, in this study, we aim to estimate the severity of PD from voice recordings of the patients using motor Unified Parkinson’s Disease Rating Scale (UPDRS) as the evaluation metric. For this purpose, we apply various speech processing algorithms to the voice signals of the patients and then use these features as input to a two-stage estimation model. The first step is to apply a wrapper-based feature selection algorithm, called Boruta, and select the most informative speech features. The second step is to feed the selected set of features to a decision tree–based boosting algorithm, extreme gradient boosting, which has been recently applied successfully in many machine learning tasks due to its generalization ability and speed. The feature selection analysis showed that the vibration pattern of the vocal fold is an important indicator of PD severity. Besides, we also investigate the effectiveness of using age and years passed since diagnosis as covariates together with speech features. The lowest mean absolute error with 3.87 was obtained by combining these covariates and speech features with prediction level fusion.

Framework for the proposed UPDRS estimation model

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Funding

This research was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) grant number 215E008, https://www.tubitak.gov.tr/en.

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  1. Hunkar C. Tunc

    Present address: Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

Authors and Affiliations

  1. Department of Computer Engineering, Bahcesehir University, 34353, Istanbul, Turkey

    Hunkar C. Tunc & C. Okan Sakar

  2. Department of Neurology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, 34098, Istanbul, Turkey

    Hulya Apaydin, Aysegul Gunduz & Melih Tutuncu

  3. Department of Biomedical Engineering, Yildiz Technical University, 34220, Istanbul, Turkey

    Gorkem Serbes

  4. Department of Computer Engineering, Bogazici University, 34342, Istanbul, Turkey

    Fikret Gurgen

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  1. Hunkar C. Tunc
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Tunc, H.C., Sakar, C.O., Apaydin, H. et al. Estimation of Parkinson’s disease severity using speech features and extreme gradient boosting. Med Biol Eng Comput 58, 2757–2773 (2020). https://doi.org/10.1007/s11517-020-02250-5

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