Objective: To use surface electromyography (EMG) to detect and characterize motor tics.

Background: Tourette syndrome is a complex neuropsychiatric disorder characterized by a combination of motor and vocal tics. The clinical evaluation of tics is fraught with difficulties relating to the variability, suppressibility, and suggestibility of tics as well as the limitations of the available evaluation tools, which rely on the subject’s recollection of tics or a short in-clinic video. These limitations have implications on both clinical practice and therapeutic investigations.

Method: Wireless EMG sensors (Trigno by Delsys™) are applied over the extremity and trunk muscles of subjects with Tourette syndrome. The EMG is rectified and referenced to baseline. Spectral analysis is estimated using 0.5 Hz bins. EMG data features that are most predictive of tics are inputted into a support vector machine. The output of the support vector machine is a binary decision “Tic” or “No Tic”. The EMG data is also used to classify the tics by body parts involved, frequency, and severity.

Results: This is an ongoing study. Evaluating a shoulder dystonic tic, power spectral analysis of the movement epochs reveals a higher EMG signal power at the level of the bilateral trapezius muscles during voluntary movements at frequencies higher than 10 Hz. On the other hand, a higher EMG signal power was noted during tic movements at the level of the bilateral biceps and sternocleidomastoid muscles especially at lower frequencies (< 40 Hz). These differences were statistically significant using a 95% confidence interval.

Conclusion: Initial analysis reveals EMG features that can distinguish a shoulder dystonic tic from a similar voluntary movement. Significant inter-subject variability is noted indicating that each subject will have characteristic EMG features specific to their own tics. The results of further evaluation and analysis as more subjects are recruited will be added by the time of the MDS meeting.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/tic-detector-role-of-electromyography/