EasiCS: the objective and fine-grained classification method of cervical spondylosis dysfunction
This work addresses the need for objective and fine-grained classification to improve diagnosis and treatment planning for cervical spondylosis patients, representing a domain-specific incremental advancement.
The paper tackled the problem of subjective and coarse-grained neck function assessment in cervical spondylosis by developing EasiCS, a framework using sEMG data with dimension reduction and clustering algorithms, which outperformed seven commonly used algorithms.
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.