Regression models for the quantification of Parkinsonian bradykinesia

Ji Won Kim, Yuri Kwon, Ju Seok Yun, Jae Hoon Heo, Gwang Moon Eom, Gye Rae Tack, Tae Hong Lim, Seong Beom Koh

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The aim of this study was to develop regression models for the quantification of parkinsonian bradykinesia. Forty patients with Parkinson's disease participated in this study. Angular velocity was measured using gyro sensor during finger tapping, forearm-rotation, and toe tapping tasks and the severity of bradykinesia was rated by two independent neurologists. Various characteristic variables were derived from the sensor signal. Stepwise multiple linear regression analysis was performed to develop models predicting the bradykinesia score with the characteristic variables as input. To evaluate the ability of the regression models to discriminate different bradykinesia scores, ANOVA and post hoc test were performed. Major determinants of the bradykinesia score differed among clinical tasks and between raters. The regression models were better than any single characteristic variable in terms of the ability to differentiate bradykinesia scores. Specifically, the regression models could differentiate all pairs of the bradykinesia scores (p<0.05) except for one pair in the finger tapping task and one pair in the toe tapping task. In contrast, any single characteristic variable was found not sensitive enough to discriminate many of the pairs, especially in case of the toe tapping task. The results suggest that the multiple regression models reflecting these differences would be beneficial for the quantification of bradykinesia because the cardinal features included in the determination of bradykinesia score differ among tasks as well as among the raters.

Original languageEnglish
Pages (from-to)S2249-S2258
JournalBio-Medical Materials and Engineering
Volume26
DOIs
Publication statusPublished - 2015 Jan 1

Fingerprint

Sensors
Angular velocity
Analysis of variance (ANOVA)
Linear regression
Regression analysis

Keywords

  • Bradykinesia
  • clinical rating
  • Parkinson's disease
  • quantification
  • stepwise multiple regression

ASJC Scopus subject areas

  • Biomaterials
  • Biomedical Engineering

Cite this

Regression models for the quantification of Parkinsonian bradykinesia. / Kim, Ji Won; Kwon, Yuri; Yun, Ju Seok; Heo, Jae Hoon; Eom, Gwang Moon; Tack, Gye Rae; Lim, Tae Hong; Koh, Seong Beom.

In: Bio-Medical Materials and Engineering, Vol. 26, 01.01.2015, p. S2249-S2258.

Research output: Contribution to journalArticle

Kim, JW, Kwon, Y, Yun, JS, Heo, JH, Eom, GM, Tack, GR, Lim, TH & Koh, SB 2015, 'Regression models for the quantification of Parkinsonian bradykinesia', Bio-Medical Materials and Engineering, vol. 26, pp. S2249-S2258. https://doi.org/10.3233/BME-151531
Kim, Ji Won ; Kwon, Yuri ; Yun, Ju Seok ; Heo, Jae Hoon ; Eom, Gwang Moon ; Tack, Gye Rae ; Lim, Tae Hong ; Koh, Seong Beom. / Regression models for the quantification of Parkinsonian bradykinesia. In: Bio-Medical Materials and Engineering. 2015 ; Vol. 26. pp. S2249-S2258.
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