Surface Electromyographic Features for Severity Classification in Facial Palsy: Insights from a German Cohort and Implications for Future Biofeedback Use

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 9; p. 2949
• Main Authors: Manzoor, Ibrahim, Popescu, Aryana, Stark, Alexia, Gorbachuk, Mykola, Spolaore, Aldo, Tatagiba, Marcos, Naros, Georgios, Machetanz, Kathrin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.05.2025; MDPI
• Subjects: Aged; Asymmetry; Biofeedback; Biofeedback training; Biofeedback, Psychology - methods; Diagnosis; Electrodes; Electromyography; facial palsy; Facial Paralysis - classification; Facial Paralysis - physiopathology; Facial Paralysis - rehabilitation; Female; grading; Humans; Machine learning; Male; Methods; Middle Aged; Paralysis, Facial; Patients; Prospective Studies; recovery; Rehabilitation; Severity of Illness Index; Surgery; Symmetry; Time series; Germany; United States > US; biofeedback; facial palsy; rehabilitation; grading; electromyography; recovery; time series features
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25092949

Facial palsy (FP) significantly impacts patients’ quality of life. The accurate classification of FP severity is crucial for personalized treatment planning. Additionally, electromyographic (EMG)-based biofeedback shows promising results in improving recovery outcomes. This prospective study aims to identify EMG time series features that can both classify FP and facilitate biofeedback. Therefore, it investigated surface EMG in FP patients and healthy controls during three different facial movements. Repeated-measures ANOVAs (rmANOVA) were conducted to examine the effects of MOTION (move/rest), SIDE (healthy/lesioned) and the House–Brackmann score (HB), across 20 distinct EMG parameters. Correlation analysis was performed between HB and the asymmetry index of EMG parameters, complemented by Fisher score calculations to assess feature relevance in distinguishing between HB levels. Overall, 55 subjects (51.2 ± 14.73 years, 35 female) were included in the study. RmANOVAs revealed a highly significant effect of MOTION across almost all movement types (p < 0.001). Integrating the findings from rmANOVA, the correlation analysis and Fisher score analysis, at least 5/20 EMG parameters were determined to be robust indicators for assessing the degree of paresis and guiding biofeedback. This study demonstrates that EMG can reliably determine severity and guide effective biofeedback in FP, and in severe cases. Our findings support the integration of EMG into personalized rehabilitation strategies. However, further studies are mandatory to improve recovery outcomes.