ArticleFrontiers in bioengineering and biotechnology2026
Machine learning analysis of surface electromyography for quantifying neuromuscular compensatory profiles and associated functional disability in neck pain.
Article in Frontiers in bioengineering and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Neck pain presents significant clinical heterogeneity, while current assessment methods lack systematic approaches for quantifying muscle function. This study established a multi-dimensional surface electromyography (sEMG) framework to quantify the predictive value of muscle function features for clinical outcomes and identify neuromuscular compensatory states. Methods: Eighty-nine participants were enrolled, including 69 chronic neck pain patients and 20 age-, sex-, and BMI-matched healthy volunteers. Surface EMG signals from six bilateral cervical muscles were recorded during functional postural control tasks in four directions. Following preprocessing and quality control, seven features spanning coordination (co-contraction index [CCI], deep-to-superficial ratio [DSR], asymmetry index [ASI]), control (sample entropy [SampEn], coefficient of variation trend [CoV_trend]), and fatigue (initial fatigue rate [IFR], median frequency slope [MDF_Slope]) dimensions were extracted. Six regression models (multiple linear regression, Ridge, Lasso, ElasticNet, Random Forest, and XGBoost) were compared using Repeated K-Fold cross-validation (5 folds × 10 repeats), with SHapley Additive exPlanations (SHAP) quantifying feature contributions. K-means clustering with multi-index validation identified distinct neuromuscular states. Results: Data quality reached 95.3% (263/276 segments). Six of seven EMG features significantly discriminated between healthy controls and patients (p < 0.05), with five showing large effect sizes (|d| > 0.8). Comparative model evaluation revealed XGBoost as the superior predictor for NDI scores (CV Conclusion: This interpretable multi-dimensional sEMG framework quantifies muscle function features' predictive value for functional disability. Severity-related neuromuscular changes may enable disease progression monitoring, while the identification of candidate compensatory states provides preliminary stratification criteria for personalized rehabilitation pending prospective validation.
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