Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches

Abstract

Millions of people worldwide are affected by Parkinson’s disease (PD), which significantly worsens their quality of life. Currently, the diagnosis is based on assessment of motor symptoms, but interest toward non-motor symptoms is increasing, as well. Among them, idiopathic hyposmia (IH) is associated with an increased risk of developing PD in healthy adults. In this work, a wearable inertial device, named SensFoot V2, was used to acquire motor data from 30 healthy subjects, 30 people with IH, and 30 PD patients while performing tasks from the MDS-UPDRS III for lower limb assessment. The most significant and non-correlated extracted parameters were selected in a feature array that can identify differences between the three groups of people. A comparative classification analysis was performed by applying three supervised machine learning algorithms. The system resulted able to distinguish between healthy and patients (specificity and recall equal to 0.967), and the people with IH can be identified as a separate class within a three-group classification (accuracy equal to 0.78). Thus, the system could support the clinician in objective assessment of PD. Further, identification of IH together with changes in motor parameters could be a non-invasive two-step approach to investigate the early onset of PD.

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Acknowledgments

This work was financially supported by DAPHNE project (Regione Toscana PAR FAS 2007-2013, Bando FAS SALUTE 2014, CUP J52I16000170002).

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Correspondence to Filippo Cavallo.

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Associate Editor Andreas Anayiotos oversaw the review of this article.

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Rovini, E., Maremmani, C., Moschetti, A. et al. Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches. Ann Biomed Eng 46, 2057–2068 (2018). https://doi.org/10.1007/s10439-018-2104-9

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Keywords

  • Decision support systems
  • Idiopathic hyposmia
  • Inertial wearable sensors
  • Motion analysis
  • Supervised learning