A multimodal approach to the quantification of kinetic tremor in Parkinson’S disease

Mateusz Szumilas , Krzysztof Lewenstein , Elżbieta Ślubowska , Stanisław Szlufik , Dariusz Koziorowski

Abstract

Parkinson’s disease results in motor impairment that deteriorates patients’ quality of life. One of the symptoms negatively interfering with daily activities is kinetic tremor which should be measured to monitor the outcome of therapy. A new instrumented method of quantification of the kinetic tremor is proposed, based on the analysis of circles drawn on a digitizing tablet by a patient. The aim of this approach is to obtain a tremor scoring equivalent to that performed by trained clinicians. Models are trained with the least absolute shrinkage and selection operator (LASSO) method to predict the tremor scores on the basis of the parameters computed from the patients’ drawings. Signal parametrization is derived from both expert knowledge and the response of an artificial neural network to the raw data, thus the approach was named multimodal. The fitted models are eventually combined into model ensembles that provide aggregated scores of the kinetic tremor captured in the drawings. The method was verified with a set of clinical data acquired from 64 Parkinson’s disease patients. Automated and objective quantification of the kinetic tremor with the presented approach yielded promising results, as the Pearson’s correlations between the visual ratings of tremor and the model predictions ranged from 0.839 to 0.890 in the best-performing models.
Author Mateusz Szumilas (FM / IMBE)
Mateusz Szumilas,,
- The Institute of Metrology and Biomedical Engineering
, Krzysztof Lewenstein (FM / IMBE)
Krzysztof Lewenstein,,
- The Institute of Metrology and Biomedical Engineering
, Elżbieta Ślubowska (FM / IMBE)
Elżbieta Ślubowska,,
- The Institute of Metrology and Biomedical Engineering
, Stanisław Szlufik - Medical University of Warsaw
Stanisław Szlufik,,
-
, Dariusz Koziorowski - Medical University of Warsaw
Dariusz Koziorowski,,
-
Journal seriesSensors, [SENSORS-BASEL], ISSN 1424-8220, e-ISSN 1424-3210
Issue year2020
Vol20
No1
Article number184
Keywords in EnglishParkinson’s disease, kinetic tremor, digitizing tablet, echo state network, machine learning
ASJC Classification1303 Biochemistry; 1602 Analytical Chemistry; 2208 Electrical and Electronic Engineering; 3107 Atomic and Molecular Physics, and Optics
DOIDOI:10.3390/s20010184
URL https://www.mdpi.com/1424-8220/20/1/184
Languageen angielski
File
sensors-20-00184.pdf 3.32 MB
Score (nominal)100
Score sourcejournalList
ScoreMinisterial score = 100.0, 19-06-2020, ArticleFromJournal
Publication indicators Scopus Citations = 0; WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.393; WoS Impact Factor: 2018 = 3.031 (2) - 2018=3.302 (5)
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