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

Published: 28 February 2024 Publication History

Abstract

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.

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

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    ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
    October 2023
    589 pages
    ISBN:9798400707988
    DOI:10.1145/3633637
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 28 February 2024

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    • the National Key Research and Development Program of China;National Defense Basic Scientific Research Program of China;Shanghai Major science and technology Project ;the Shanghai Industrial Collaborative Technology Innovation Project

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