Digital Biomarker for Muscle Function Assessment Using Surface Electromyography With Electrical Stimulation and a Non-Invasive Wearable Device

• Main Authors: Kwangsub Song (Author), Hyung Eun Shin (Author), Wookhyun Park (Author), Daehyun Lee (Author), Jaeyoung Jang (Author), Ga Yang Shim (Author), Sangui Choi (Author), Miji Kim (Author), Hooman Lee (Author), Chang Won Won (Author)
• Format: Book
• Published: IEEE, 2024-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1534-4320; 1558-0210; 10.1109/TNSRE.2024.3444890
• Keywords: Digital biomarker; muscle mass; muscle strength; stimulated muscle contraction signal; deep neural network; Medical technology; R855-855.5; Therapeutics. Pharmacology; RM1-950
• Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 3048-3058 (2024)

Sarcopenia is a comprehensive degenerative disease with the progressive loss of skeletal muscle mass with age, accompanied by the loss of muscle strength and muscle dysfunction. Individuals with unmanaged sarcopenia may experience adverse outcomes. Periodically monitoring muscle function to detect muscle degeneration caused by sarcopenia and treating degenerated muscles is essential. We proposed a digital biomarker measurement technique using surface electromyography (sEMG) with electrical stimulation and wearable device to conveniently monitor muscle function at home. When motor neurons and muscle fibers are electrically stimulated, stimulated muscle contraction signals (SMCSs) can be obtained using an sEMG sensor. As motor neuron activation is important for muscle contraction and strength, their action potentials for electrical stimulation represent the muscle function. Thus, the SMCSs are closely related to muscle function, presumptively. Using the SMCSs data, a feature vector concatenating spectrogram-based features and deep learning features extracted from a convolutional neural network model using continuous wavelet transform images was used as the input to train a regression model for measuring the digital biomarker. To verify muscle function measurement technique, we recruited 98 healthy participants aged 20–60 years including 48 [49%] men who volunteered for this study. The Pearson correlation coefficient between the label and model estimates was 0.89, suggesting that the proposed model can robustly estimate the label using SMCSs, with mean error and standard deviation of -0.06 and 0.68, respectively. In conclusion, measuring muscle function using the proposed system that involves SMCSs is feasible.