Quantifying Parkinson’s disease finger-tapping severity by extracting and synthesizing finger motion properties
- 404 Downloads
We propose a novel index of Parkinson’s disease (PD) finger-tapping severity, called “PDFTsi,” for quantifying the severity of symptoms related to the finger tapping of PD patients with high accuracy. To validate the efficacy of PDFTsi, the finger-tapping movements of normal controls and PD patients were measured by using magnetic sensors, and 21 characteristics were extracted from the finger-tapping waveforms. To distinguish motor deterioration due to PD from that due to aging, the aging effect on finger tapping was removed from these characteristics. Principal component analysis (PCA) was applied to the age-normalized characteristics, and principal components that represented the motion properties of finger tapping were calculated. Multiple linear regression (MLR) with stepwise variable selection was applied to the principal components, and PDFTsi was calculated. The calculated PDFTsi indicates that PDFTsi has a high estimation ability, namely a mean square error of 0.45. The estimation ability of PDFTsi is higher than that of the alternative method, MLR with stepwise regression selection without PCA, namely a mean square error of 1.30. This result suggests that PDFTsi can quantify PD finger-tapping severity accurately. Furthermore, the result of interpreting a model for calculating PDFTsi indicated that motion wideness and rhythm disorder are important for estimating PD finger-tapping severity.
KeywordsParkinson’s disease Finger tapping Magnetic sensor Principal component analysis Multiple linear regression
We thank Ms. Jonghin Park at Osaka University and all the students at Hiroshima University for helping us collect the finger-tapping data.
- 2.Alty J, Jamieson S, Lones M, Smith S (2012) How slow is too slow? objective measurement of bradykinesia in Parkinson's disease using novel noninvasive devices. In: Movement disorders, vol 27, Wiley-Blackwell 111 River ST, Hoboken 07030–5774, NJ USA, pp S91–S92Google Scholar
- 3.Bishop CM, Nasrabadi NM (2006) Pattern recognition and machine learning. Chap Principal component analysis. Springer, New YorkGoogle Scholar
- 5.Collins RC (1997) Neurology. Saunders Company, PhiladelphiaGoogle Scholar
- 9.Goetz CG, Fahn S, Martinez-Martin P, Poewe W, Sampaio C, Stebbins GT, Stern MB, Tilley BC, Dodel R, Dubois B et al (2007) Movement disorder society-sponsored revision of the unified Parkinson's disease rating scale (MDS-UPDRS): process, format, and clinimetric testing plan. Mov Disord 22(1):41–47CrossRefPubMedGoogle Scholar
- 10.Goetz CG, Stebbins GT, Wolff D, DeLeeuw W, Bronte-Stewart H, Elble R, Hallett M, Nutt J, Ramig L, Sanger T et al (2009) Testing objective measures of motor impairment in early Parkinson's disease: feasibility study of an at-home testing device. Mov Disord 24(4):551–556CrossRefPubMedPubMedCentralGoogle Scholar
- 11.Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, Poewe W, Sampaio C, Stern MB, Dodel R et al (2008) Movement disorder society-sponsored revision of the Unified Parkinson's disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord 23(15):2129–2170CrossRefPubMedGoogle Scholar
- 14.Jolliffe IT (1982) A note on the use of principal components in regression. J R Stat Soc Ser C Appl Stat 31(3):300–303Google Scholar
- 23.Okuno R, Yokoe M, Akazawa K, Abe K, Sakoda S (2006) Finger taps movement acceleration measurement system for quantitative diagnosis of Parkinson's disease. In: Proceedings of the twenty eighth annual international conference of the IEEE Engineering in Medicine and Biology Society, pp. 6623–6626 (2006)Google Scholar
- 25.Sheather S (2009) A modern approach to regression with R, chap. Multiple linear regression (Chapter 5) and variable selection (Chapter 7), pp. 125.147 and 227–261. Springer, New York (2009)Google Scholar
- 26.Shima K, Kan E, Tsuji T, Kandori A, Yokoe M, Sakoda S (2008) A motor function evaluation system for finger tapping movements using magnetic sensors. The Jpn J Med Instrum 78(12):909–918 (In Japanese)Google Scholar
- 27.Shima K, Tsuji T, Kan E, Kandori A, Yokoe M, Sakoda S (2008) Measurement and evaluation of finger tapping movements using magnetic sensors. In: Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual international conference of the IEEE, pp 5628–5631 (2008)Google Scholar