A Novel Automatic Method for Monitoring Tourette Motor Tics Through a Wearable Device
Identifieur interne : 002821 ( Main/Merge ); précédent : 002820; suivant : 002822A Novel Automatic Method for Monitoring Tourette Motor Tics Through a Wearable Device
Auteurs : Michel Bernabei [Italie] ; Ezio Preatoni [Italie] ; Martin Mendez [Italie, Mexique] ; Luca Piccini [Italie] ; Mauro Porta [Italie] ; Giuseppe Andreoni [Italie]Source :
- Movement disorders [ 0885-3185 ] ; 2010.
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- Pascal (Inist)
English descriptors
- KwdEn :
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.
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<series><title level="j" type="main">Movement disorders</title>
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<front><div type="abstract" xml:lang="en">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.</div>
</front>
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<tree><country name="Italie"><region name="Lombardie"><name sortKey="Bernabei, Michel" sort="Bernabei, Michel" uniqKey="Bernabei M" first="Michel" last="Bernabei">Michel Bernabei</name>
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<name sortKey="Mendez, Martin" sort="Mendez, Martin" uniqKey="Mendez M" first="Martin" last="Mendez">Martin Mendez</name>
<name sortKey="Piccini, Luca" sort="Piccini, Luca" uniqKey="Piccini L" first="Luca" last="Piccini">Luca Piccini</name>
<name sortKey="Porta, Mauro" sort="Porta, Mauro" uniqKey="Porta M" first="Mauro" last="Porta">Mauro Porta</name>
<name sortKey="Preatoni, Ezio" sort="Preatoni, Ezio" uniqKey="Preatoni E" first="Ezio" last="Preatoni">Ezio Preatoni</name>
<name sortKey="Preatoni, Ezio" sort="Preatoni, Ezio" uniqKey="Preatoni E" first="Ezio" last="Preatoni">Ezio Preatoni</name>
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<country name="Mexique"><noRegion><name sortKey="Mendez, Martin" sort="Mendez, Martin" uniqKey="Mendez M" first="Martin" last="Mendez">Martin Mendez</name>
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