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

doi:10.3233/BME-151531

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Regression models for the quantification of Parkinsonian bradykinesia

Journal article

Open access

Peer reviewed

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 and Seong-Beom Koh

Bio-medical materials and engineering, Vol.26 Suppl 1(s1), pp.S2249-S2258

2015

DOI: 10.3233/BME-151531

PMID: 26406005

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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.

Parkinson Disease - complications

Regression Analysis

Analysis of Variance

Humans

Hypokinesia - diagnosis

Middle Aged

Hypokinesia - complications

Parkinson Disease - diagnosis

Female

Male

Aged

Models, Statistical

Details

Title: Subtitle: Regression models for the quantification of Parkinsonian bradykinesia
Creators: Ji-Won Kim - School of Biomedical Engineering, Choonju, 380-701, Korea
Yuri Kwon - School of Biomedical Engineering, Choonju, 380-701, Korea
Ju-Seok Yun - School of Biomedical Engineering, Choonju, 380-701, Korea
Jae-Hoon Heo - School of Biomedical Engineering, Choonju, 380-701, Korea
Gwang-Moon Eom - Research Institute of Biomedical Engineering, Konkuk University, Choonju, 380-701, Korea
Gye-Rae Tack - Research Institute of Biomedical Engineering, Konkuk University, Choonju, 380-701, Korea
Tae-Hong Lim - Department of Biomedical Engineering, University of Iowa, USA
Seong-Beom Koh - Department of Neurology, College of Medicine, Korea University, Seoul, Korea
Resource Type: Journal article
Publication Details: Bio-medical materials and engineering, Vol.26 Suppl 1(s1), pp.S2249-S2258
DOI: 10.3233/BME-151531
PMID: 26406005
NLM abbreviation: Biomed Mater Eng
ISSN: 0959-2989
eISSN: 1878-3619
Publisher: Netherlands
Language: English
Date published: 2015
Academic Unit: Roy J. Carver Department of Biomedical Engineering
Record Identifier: 9984064111202771

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