Design and Evaluation of a Custom-Made Electromyographic Biofeedback System for Facial Rehabilitation

Abstract

BackgroundIn the rehabilitation of postoperative facial palsy, physical therapy is of paramount importance. However, in the early rehabilitation phase, voluntary movements are often limited, and thus, the motivation of patients is impacted. In these situations, biofeedback of facial electromyographic (EMG) signals enables the visual representation of the rehabilitation progress, even without apparent facial movements. In the present study, we designed and evaluated a custom-made EMG biofeedback system enabling cost-effective facial rehabilitation.MethodsThis prospective study describes a custom-made EMG system, consisting of a microcontroller board and muscle sensors, which was used to record the EMG of frontal and zygomatic facial muscles during frowning and smiling. First, the mean EMG amplitudes and movement onset detection rates (ACC) achieved with the custom-made EMG system were compared with a commercial EMG device in 12 healthy subjects. Subsequently, the custom-made device was applied to 12 patients with and without postoperative facial paresis after neurosurgical intervention. Here, the ratio [laterality index (LI)] between the mean EMG amplitude of the healthy and affected side was calculated and related to the facial function as measured by the House and Brackmann scale (H&B) ranging from 1 (normal) to 6 (total paralysis).ResultsIn healthy subjects, a good correlation was measured between the mean EMG amplitudes of the custom-made and commercial EMG device for both frontal (r = 0.84, p = 0.001) and zygomatic muscles (r = 0.8, p = 0.002). In patients, the LI of the frontal and zygomatic muscles correlated significantly with the H&B (r = −0.83, p = 0.001 and r = −0.65, p = 0.023). The ACC of the custom-made EMG system varied between 65 and 79% depending on the recorded muscle and cohort.ConclusionThe present study demonstrates a good application potential of our custom-made EMG biofeedback device to detect facial EMG activity in healthy subjects as well as patients with facial palsies. There is a correlation between the electrophysiological measurements and the clinical outcome. Such a device might enable cost-efficient home-based facial EMG biofeedback. However, movement detection accuracy should be improved in future studies to reach ranges of commercial devices.