Literature DB >> 24625831

Kinematic analysis in patients with Parkinson's disease and SWEDD.

Myung Jun Lee1, Sha Lom Kim1, Chul Hyoung Lyoo1, Myung Sik Lee1.   

Abstract

BACKGROUND AND OBJECTIVES: The differential diagnosis between mild Parkinson's disease (PD) and Scan Without Evidence of Dopaminergic Deficit(SWEDD) is challenging. Progressive reduction in amplitude and speed of finger tapping (sequence effect) has been considered as the most useful sign for discriminating PD from SWEDD. However, a video analysis reported that sequence effect is a major confounding factor for the misdiagnosis of PD. Our objective was to perform a kinematic analysis of finger tapping to explore parameters for distinguishing between patients with PD and SWEDD.
METHODS: We enrolled 14 patients with PD, 17 patients with SWEDD and 18 age- and sex-matched healthy controls. Amplitude, speed and frequency of finger tapping were measured using gyroscopes, and the means, decrement and variability in kinematic parameters for specific tapping duration were calculated.
RESULTS: Compared to SWEDD group, PD group showed more decrement in amplitude and speed of the first 20 taps, more decrement in frequency after 20 taps and more variability in speed of 15 seconds of taps. However, none of parameters was a practically useful indicator distinguishing individual patients with PD from those with SWEDD.
CONCLUSIONS: Analysis of finger tapping, even using an apparatus, is not useful for distinguishing mild PD and SWEDD.

Entities:  

Keywords:  Parkinson's disease; SWEDD; finger tapping; kinematic analysis

Mesh:

Year:  2014        PMID: 24625831     DOI: 10.3233/JPD-130233

Source DB:  PubMed          Journal:  J Parkinsons Dis        ISSN: 1877-7171            Impact factor:   5.568


  2 in total

1.  Computer-Aided Diagnosis of Parkinson's Disease Using Enhanced Probabilistic Neural Network.

Authors:  Thomas J Hirschauer; Hojjat Adeli; John A Buford
Journal:  J Med Syst       Date:  2015-09-29       Impact factor: 4.460

2.  Evaluation for Parkinsonian Bradykinesia by deep learning modeling of kinematic parameters.

Authors:  Dong Jun Park; Jun Woo Lee; Myung Jun Lee; Se Jin Ahn; Jiyoung Kim; Gyu Lee Kim; Young Jin Ra; Yu Na Cho; Weui Bong Jeong
Journal:  J Neural Transm (Vienna)       Date:  2021-01-28       Impact factor: 3.575

  2 in total

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