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

Michel Bernabei, Ezio Preatoni, Martin Mendez, Luca Piccini, Mauro Porta, Giuseppe Andreoni

Research output: Contribution to journalArticle

9 Citations (Scopus)

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 motortics 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.

Original languageEnglish
Pages (from-to)1967-1972
Number of pages6
JournalMovement Disorders
Volume25
Issue number12
DOIs
Publication statusPublished - Sep 15 2010

Fingerprint

Tics
Tourette Syndrome
Equipment and Supplies
Gold
Least-Squares Analysis
Walking
Noise
Clinical Trials
Physicians
Sensitivity and Specificity
Population

Keywords

  • Accelerometers
  • Automatic tic detection
  • Tourette syndrome
  • Wearable monitoring

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology

Cite this

Bernabei, M., Preatoni, E., Mendez, M., Piccini, L., Porta, M., & Andreoni, G. (2010). A novel automatic method for monitoring tourette motor tics through a wearable device. Movement Disorders, 25(12), 1967-1972. https://doi.org/10.1002/mds.23188

A novel automatic method for monitoring tourette motor tics through a wearable device. / Bernabei, Michel; Preatoni, Ezio; Mendez, Martin; Piccini, Luca; Porta, Mauro; Andreoni, Giuseppe.

In: Movement Disorders, Vol. 25, No. 12, 15.09.2010, p. 1967-1972.

Research output: Contribution to journalArticle

Bernabei, M, Preatoni, E, Mendez, M, Piccini, L, Porta, M & Andreoni, G 2010, 'A novel automatic method for monitoring tourette motor tics through a wearable device', Movement Disorders, vol. 25, no. 12, pp. 1967-1972. https://doi.org/10.1002/mds.23188
Bernabei, Michel ; Preatoni, Ezio ; Mendez, Martin ; Piccini, Luca ; Porta, Mauro ; Andreoni, Giuseppe. / A novel automatic method for monitoring tourette motor tics through a wearable device. In: Movement Disorders. 2010 ; Vol. 25, No. 12. pp. 1967-1972.
@article{3177bd5d82ed4d5899c3cd05ae6ebdf8,
title = "A novel automatic method for monitoring tourette motor tics through a wearable device",
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 motortics 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.",
keywords = "Accelerometers, Automatic tic detection, Tourette syndrome, Wearable monitoring",
author = "Michel Bernabei and Ezio Preatoni and Martin Mendez and Luca Piccini and Mauro Porta and Giuseppe Andreoni",
year = "2010",
month = "9",
day = "15",
doi = "10.1002/mds.23188",
language = "English",
volume = "25",
pages = "1967--1972",
journal = "Movement Disorders",
issn = "0885-3185",
publisher = "John Wiley and Sons Inc.",
number = "12",

}

TY - JOUR

T1 - A novel automatic method for monitoring tourette motor tics through a wearable device

AU - Bernabei, Michel

AU - Preatoni, Ezio

AU - Mendez, Martin

AU - Piccini, Luca

AU - Porta, Mauro

AU - Andreoni, Giuseppe

PY - 2010/9/15

Y1 - 2010/9/15

N2 - 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 motortics 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.

AB - 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 motortics 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.

KW - Accelerometers

KW - Automatic tic detection

KW - Tourette syndrome

KW - Wearable monitoring

UR - http://www.scopus.com/inward/record.url?scp=77956809758&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956809758&partnerID=8YFLogxK

U2 - 10.1002/mds.23188

DO - 10.1002/mds.23188

M3 - Article

C2 - 20669298

AN - SCOPUS:77956809758

VL - 25

SP - 1967

EP - 1972

JO - Movement Disorders

JF - Movement Disorders

SN - 0885-3185

IS - 12

ER -