A novel automatic method for monitoring Tourette motor tics through a wearable device

Authors

  • Michel Bernabei MSc,

    1. Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy
    2. Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy
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  • Ezio Preatoni PhD,

    Corresponding author
    1. Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy
    2. Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy
    • Dipartimento INDACO, Politecnico di Milano, Piazza Leonardo Da Vinci, 32 – 20133 Milano, Italy
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  • Martin Mendez PhD,

    1. Dipartimento di Bioingegneria, Politecnico di Milano, Milan, Italy
    2. Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico
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  • Luca Piccini PhD,

    1. Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy
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  • Mauro Porta MD,

    1. Department of Neurology, Tourette Centre, IRCCS “Galeazzi,” Milan, Italy
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  • Giuseppe Andreoni PhD

    1. Dipartimento di Industrial Design, Arti, Comunicazione e Moda (INDACO), Politecnico di Milano, Milan, Italy
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  • Potential conflict of interest: None of the authors presents conflict of interests or has received financial support for carrying out the present research.

Abstract

The aim of this study was to propose a novel automatic method for quantifying motor-tics caused by the Tourette Syndrome (TS). In this preliminary report, the feasibility of the monitoring process was tested over a series of standard clinical trials in a population of 12 subjects affected by TS. A wearable instrument with an embedded three-axial accelerometer was used to detect and classify motor tics during standing and walking activities. An algorithm was devised to analyze acceleration data by: eliminating noise; detecting peaks connected to pathological events; and classifying intensity and frequency of motor tics into quantitative scores. These indexes were compared with the video-based ones provided by expert clinicians, which were taken as the gold-standard. Sensitivity, specificity, and accuracy of tic detection were estimated, and an agreement analysis was performed through the least square regression and the Bland-Altman test. The tic recognition algorithm showed sensitivity = 80.8% ± 8.5% (mean ± SD), specificity = 75.8% ± 17.3%, and accuracy = 80.5% ± 12.2%. The agreement study showed that automatic detection tended to overestimate the number of tics occurred. Although, it appeared this may be a systematic error due to the different recognition principles of the wearable and video-based systems. Furthermore, there was substantial concurrency with the gold-standard in estimating the severity indexes. The proposed methodology gave promising performances in terms of automatic motor-tics detection and classification in a standard clinical context. The system may provide physicians with a quantitative aid for TS assessment. Further developments will focus on the extension of its application to everyday long-term monitoring out of clinical environments. © 2010 Movement Disorder Society

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