title: Quantifying Parkinson's disease finger-tapping severity by extracting and synthesizing finger motion properties.
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.
authors.affiliation: Research & Development Group, Center for Technology Innovation - Healthcare, Hitachi Ltd., 1-280 Higashi-Koigakubo, Kokubunji, Tokyo, Japan. yuko.sano.hd@hitachi.com.
authors.forename: Yuko
authors.initials: Y
authors.lastname: Sano
authors.affiliation: Research & Development Group, Center for Technology Innovation - Healthcare, Hitachi Ltd., 1-280 Higashi-Koigakubo, Kokubunji, Tokyo, Japan.
authors.forename: Akihiko
authors.initials: A
authors.lastname: Kandori
authors.affiliation: Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, Japan.
authors.forename: Keisuke
authors.initials: K
authors.lastname: Shima
authors.affiliation: Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, Japan.
authors.forename: Yuki
authors.initials: Y
authors.lastname: Yamaguchi
authors.affiliation: Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, Japan.
authors.forename: Toshio
authors.initials: T
authors.lastname: Tsuji
authors.affiliation: Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan.
authors.forename: Masafumi
authors.initials: M
authors.lastname: Noda
authors.affiliation: Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan.
authors.forename: Fumiko
authors.initials: F
authors.lastname: Higashikawa
authors.affiliation: Graduate School of Medicine, Osaka University, 1-7 Yamadaoka, Suita, Osaka, Japan.
authors.forename: Masaru
authors.initials: M
authors.lastname: Yokoe
authors.affiliation: National Hospital Organization, Toneyama Hospital, 5-1-1 Toneyama, Toyonaka, Osaka, Japan.
authors.forename: Saburo
authors.initials: S
authors.lastname: Sakoda
mesh_terms: D000368:Aged; D000465:Algorithms; D005260:Female; D005385:Fingers / Q000503:physiopathology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009038:Motion; D009068:Movement; D010300:Parkinson Disease / Q000175:diagnosis*; D025341:Principal Component Analysis; D058067:Wavelet Analysis
keywords: Finger tapping; Magnetic sensor; Multiple linear regression; Parkinson’s disease; Principal component analysis