Digital Biomarkers for Diagnosis of Muscle Disorders Using Stimulated Muscle Contraction Signal

Abstract

We propose a digital biomarker related to muscle strength and muscle endurance (DB/MS and DB/ME) for the diagnosis of muscle disorders based on a multi-layer perceptron (MLP) using stimulated muscle contraction. When muscle mass is reduced in patients with muscle-related diseases or disorders, measurement of DBs that are related to muscle strength and endurance is needed to suitably recover damaged muscles through rehabilitation training. Furthermore, it is difficult to measure DBs using traditional methods at home without an expert; moreover, the measuring equipment is expensive. Additionally, because traditional measurements depend on the subjectb’s volition, we propose a DB measurement technique that is unaffected by the subjectb’s volition. To achieve this, we employed an impact response signal (IRS) based on multi-frequency electrical stimulation (MFES) using an electromyography sensor. The feature vector was then extracted using the signal. Because the IRS is obtained from stimulated muscle contraction, which is caused by electrical stimulation, it provides biomedical information about the muscle. Finally, to estimate the strength and endurance of the muscle, the feature vector was passed through the DB estimation model learned through the MLP. To evaluate the performance of the DB measurement algorithm, we collected the MFES-based IRS database for 50 subjects and tested the model with quantitative evaluation methods using the reference for the DB. The reference was measured using torque equipment. The results were compared with the reference, indicating that it is possible to check for muscle disorders which cause decreased physical performance using the proposed algorithm.

Keywords

• Digital biomarker
• diagnosis of muscle disorders
• stimulated muscle contraction
• impact response signal
• multi-frequency electrical stimulation
• deep neural network