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Quantifying Parkinson’s disease finger-tapping severity by extracting and synthesizing finger motion properties

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Abstract

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.

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Notes

  1. As for the PD patients, the measurement duration was 30 s. The finger tapping of the first 15 s was used.

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Acknowledgments

We thank Ms. Jonghin Park at Osaka University and all the students at Hiroshima University for helping us collect the finger-tapping data.

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Authors and Affiliations

  1. Research & Development Group, Center for Technology Innovation - Healthcare, Hitachi Ltd., 1-280 Higashi-Koigakubo, Kokubunji, Tokyo, Japan

    Yuko Sano & Akihiko Kandori

  2. Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, Japan

    Keisuke Shima, Yuki Yamaguchi & Toshio Tsuji

  3. Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan

    Masafumi Noda & Fumiko Higashikawa

  4. Graduate School of Medicine, Osaka University, 1-7 Yamadaoka, Suita, Osaka, Japan

    Masaru Yokoe

  5. National Hospital Organization, Toneyama Hospital, 5-1-1 Toneyama, Toyonaka, Osaka, Japan

    Saburo Sakoda

Authors
  1. Yuko Sano
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  2. Akihiko Kandori
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  3. Keisuke Shima
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  4. Yuki Yamaguchi
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  5. Toshio Tsuji
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  6. Masafumi Noda
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  7. Fumiko Higashikawa
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  8. Masaru Yokoe
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  9. Saburo Sakoda
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Corresponding author

Correspondence to Yuko Sano.

Appendix

Appendix

Basic statistics of the data used in this study are shown in the following. The correlation between two age-normalized characteristics is shown in Fig. 8. PCA was applied to age-normalized characteristics that are correlated with each other to extract independent motion properties. The comparison between average of the characteristics of normal controls and PD patients is shown in Fig. 9.

Fig. 8
figure 8

Correlation coefficients between two age-normalized characteristics. Characteristic numbers correspond to Fig. 3

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Fig. 9
figure 9

Comparison between average of characteristics of normal controls and PD patients. Characteristic numbers correspond to Fig. 3. Averages (bar) and standard deviations (error bars) are standardized by average of the normal controls’ average to compare between characteristics

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Sano, Y., Kandori, A., Shima, K. et al. Quantifying Parkinson’s disease finger-tapping severity by extracting and synthesizing finger motion properties. Med Biol Eng Comput 54, 953–965 (2016). https://doi.org/10.1007/s11517-016-1467-z

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  • DOI: https://doi.org/10.1007/s11517-016-1467-z

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