Electrophysiological Signatures of Sarcopenia: A Systematic Review of sEMG Features, Fatigue Indices and AI-Based Classifiers.

Age-related sarcopenia involves structural and functional neuromuscular changes. Electrophysiological measures from surface electromyography (sEMG) capture activation dynamics, spectral fatigue indices and motor unit properties that may constitute objective signatures of sarcopenia. The objective of this work is to systematically review sEMG features, fatigability metrics and AI-based classification/regression approaches reported for sarcopenia assessment between 2019 and 2026. PRISMA guidelines were followed, and IEEE Xplore, PubMed and Scopus were searched for open access human studies. Extracted information comprised sample characteristics, muscles and tasks, signal acquisition and preprocessing, extracted time/frequency/time-frequency and motor unit features, fatigue metrics, machine learning pipelines, validation schemes and dataset accessibility. Studies were classified into activation, fatigue, ML, and neural control groups; risk of bias was assessed. A total of 12 studies fulfilled the inclusion criteria. Recurrent electrophysiological signatures included reduced distal activation with compensatory proximal recruitment and higher antagonist co-activation; diminished MF/IMDF fatigue slopes indicative of Type II fiber loss and altered motor unit recruitment; motor unit analyses revealed decreased discharge rates and larger MUAP amplitudes. AI-based models combining multidomain features (time, spectral, CWT/EMD, and motor unit metrics) yielded reasonable screening performance (AUC/accuracy 0.73-0.89) when using robust feature selection and explainability tools. Heterogeneity in acquisition, normalization, small cohorts and sparse data sharing limited comparability and external validity. The findings indicate that sEMG-derived electrophysiological signatures are promising for sarcopenia detection and monitoring. To translate signatures into reliable clinical tools, standardized protocols, larger shared datasets, multimodal features, including motor unit metrics, and rigorous external validation of AI models are required.