A novel automatic method for monitoring Tourette motor tics through a wearable device.
Identifieur interne : 002D44 ( Ncbi/Curation ); précédent : 002D43; suivant : 002D45A novel automatic method for monitoring Tourette motor tics through a wearable device.
Auteurs : Michel Bernabei [Italie] ; Ezio Preatoni ; Martin Mendez ; Luca Piccini ; Mauro Porta ; Giuseppe AndreoniSource :
- Movement disorders : official journal of the Movement Disorder Society [ 1531-8257 ] ; 2010.
English descriptors
- KwdEn :
- Adolescent, Adult, Female, Humans, Male, Middle Aged, Monitoring, Ambulatory (instrumentation), Monitoring, Ambulatory (methods), Sensitivity and Specificity, Severity of Illness Index, Tics (diagnosis), Tics (physiopathology), Tourette Syndrome (diagnosis), Tourette Syndrome (physiopathology), Video Recording.
- MESH :
- diagnosis : Tics, Tourette Syndrome.
- instrumentation : Monitoring, Ambulatory.
- methods : Monitoring, Ambulatory.
- physiopathology : Tics, Tourette Syndrome.
- Adolescent, Adult, Female, Humans, Male, Middle Aged, Sensitivity and Specificity, Severity of Illness Index, Video Recording.
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.
DOI: 10.1002/mds.23188
PubMed: 20669298
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pubmed:20669298Le document en format XML
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<author><name sortKey="Preatoni, Ezio" sort="Preatoni, Ezio" uniqKey="Preatoni E" first="Ezio" last="Preatoni">Ezio Preatoni</name>
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<author><name sortKey="Mendez, Martin" sort="Mendez, Martin" uniqKey="Mendez M" first="Martin" last="Mendez">Martin Mendez</name>
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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>
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