Comparative Analysis of Classification Methods for Diagnosing Myasthenia Gravis Based on Lumbar Electromyography.

Sarcopenia is a disease of the elderly characterized by a loss of muscle strength and muscle mass that significantly affects health status, functional independence and quality of life in older adults. In order to reduce the negative impact of the disease on individuals, diagnosis alone is not enough; we need a deeper understanding of the disease. With the widespread use of smart and minimally invasive wearable devices, surface electromyography (sEMG) is becoming increasingly important in the prevention and diagnosis of sarcopenia. The availability of these technologies provides a non-invasive way to monitor muscle activity and provide detailed information about muscle health status. The application of sEMG allows us to gain a more comprehensive understanding of the development and progression of sarcopenia so that we can adopt appropriate prevention and treatment strategies to improve the quality of life of the elderly. In this study, surface EMG signals were extracted from the multifidus and lumbar iliac rib muscles and used to diagnose the presence of sarcopenia. Twenty-eight subjects aged ≥50 years were recruited through Shanghai Pudong Gongli Hospital and compared using multiple classifiers, of which the support vector machine classifier had the highest accuracy rate.