2019/05/15 by Nana Wang, Li Cui, Wang, Nana +9
Engineering · Medicine · #Cervical and Thoracic Myelopathy #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Imaging and Analysis #Stroke Rehabilitation and Recovery
paper · pdf · doi:10.48550/arxiv.1905.05987
openalex publication_date 2019/05/15 · openalex created_date 2019/05/29 · openalex updated_date 2026/07/28
The precise diagnosis is of great significance in developing precise treatment plans to restore neck function and reduce the burden posed by the cervical spondylosis (CS). However, the current available neck function assessment method are subjective and coarse-grained. In this paper, based on the relationship among CS, cervical structure, cervical vertebra function, and surface electromyography (sEMG), we seek to develop a clustering algorithms on the sEMG data set collected from the clinical environment and implement the division. We proposed and developed the framework EasiCS, which consists of dimension reduction, clustering algorithm EasiSOM, spectral clustering algorithm EasiSC. The EasiCS outperform the commonly used seven algorithms overall.