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Discrimination between healthy and patients with Parkinson’s disease from hand resting activity using inertial measurement unit

Authors
  • Peres, Luciano Brinck1
  • Calil, Bruno Coelho2
  • da Silva, Ana Paula Sousa Paixão Barroso...3
  • Dionísio, Valdeci Carlos1
  • Vieira, Marcus Fraga4
  • de Oliveira Andrade, Adriano1
  • Pereira, Adriano Alves1
  • 1 Federal University of Uberlândia, Uberlândia, Brazil , Uberlândia (Brazil)
  • 2 UNA Uberlândia University Center, Uberlândia, Brazil , Uberlândia (Brazil)
  • 3 Mineiros University Center, Mineiros, Brazil , Mineiros (Brazil)
  • 4 Federal University of Goiás, Goiânia, Brazil , Goiânia (Brazil)
Type
Published Article
Journal
BioMedical Engineering OnLine
Publisher
Springer (Biomed Central Ltd.)
Publication Date
May 22, 2021
Volume
20
Issue
1
Identifiers
DOI: 10.1186/s12938-021-00888-2
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundParkinson’s disease (PD) is a neurological disease that affects the motor system. The associated motor symptoms are muscle rigidity or stiffness, bradykinesia, tremors, and gait disturbances. The correct diagnosis, especially in the initial stages, is fundamental to the life quality of the individual with PD. However, the methods used for diagnosis of PD are still based on subjective criteria. As a result, the objective of this study is the proposal of a method for the discrimination of individuals with PD (in the initial stages of the disease) from healthy groups, based on the inertial sensor recordings.MethodsA total of 27 participants were selected, 15 individuals previously diagnosed with PD and 12 healthy individuals. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Different numbers of features were used to compare the values of sensitivity, specificity, precision, and accuracy of the classifiers. For group classification, 4 classifiers were used and compared, those being [Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes (NB)].ResultsWhen all individuals with PD were analyzed, the best performance for sensitivity and accuracy (0.875 and 0.800, respectively) was found in the SVM classifier, fed with 20% and 10% of the features, respectively, while the best performance for specificity and precision (0.933 and 0.917, respectively) was associated with the RF classifier fed with 20% of all the features. When only individuals with PD and score 1 on the Hoehn and Yahr scale (HY) were analyzed, the best performances for sensitivity, precision and accuracy (0.933, 0.778 and 0.848, respectively) were from the SVM classifier, fed with 40% of all features, and the best result for precision (0.800) was connected to the NB classifier, fed with 20% of all features.ConclusionThrough an analysis of all individuals in this study with PD, the best classifier for the detection of PD (sensitivity) was the SVM fed with 20% of the features and the best classifier for ruling out PD (specificity) was the RF classifier fed with 20% of the features. When analyzing individuals with PD and score HY = 1, the SVM classifier was superior across the sensitivity, precision, and accuracy, and the NB classifier was superior in the specificity. The obtained result indicates that objective methods can be applied to help in the evaluation of PD.

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