MGS-Net: Fusing Global and Local Feature Enhancements for Healthcare Education Management of Myasthenia Gravis Using Speech Data

Myasthenia gravis (MG) is a neurological disease that is difficult to diagnose and requires long-term management. The progression of this disease is reflected to some extent in changes in speech, such as hoarseness and articulation disorders. However, it is difficult for general neurologists to grasp the diagnostic patterns of such rare diseases, especially in underdeveloped regions. As an emerging field, speech-based intelligent diagnostic assistance provides a safe, non-invasive, and convenient solution for healthcare education management. To this end, we firstly constructed a novel Chinese speech dataset of myasthenia gravis patients (MGCS). Then we proposed a network named Myasthenia Gravis Speech Net (MGS-Net) for the classification of myasthenia gravis pathological speech, which is mainly composed of two blocks: the Local Feature Enhancement (LFE) block and the Feedforward Dense (FFD) block. The LFE block extracts temporal local features using a sliding window approach, while the FFD block captures the global representation of the data. Compared to existing methods, our pipeline achieves an accuracy of 98.75% and a recall rate of 99.17%. We validated the effectiveness of existing acoustic feature sets in pathological speech classification of MG, which will provide an important tool for health education management of neurological diseases.