Ensemble Clustering-based Cervical Spondylosis Fine-classification

State-of-the-art and state-of-the-practice clinical work fail to fine-classify early cervical spondylosis (CS) illness and hence can not provide personalized treatment. Surface electromyography (sEMG) is an essential physiological signal that can locate muscle problems associated with cervical spine dysfunction, and it is promising to provide sEMG-based CS fine-classification. However, the state-of-the-art clustering approaches on sEMG data have the following drawbacks: (1) the clustering results are unstable due to different algorithms or multiple values of a resolution parameter. (2) The samples of the same individual in the same state are clustered in different groups. This paper proposes an individual-centered three-layer ensemble clustering framework (in short, ITECF) to fine-classify cervical spondylosis (CS). ITECF mainly consists of three parts: base clusters generation, individual consensus, base cluster consensus. We define an individual consistency index named ICS to measure the consistency of samples of the same individual in the same partition and propose an ICS-based consensus to transform the sample-centered base clusters into individual-centered ones for partition selection. We evaluate our approaches against five clustering algorithms and five ensemble clustering frameworks on the three real-world sEMG data set using four metrics: Silhouette Coefficient (SC), Davies Bouldin score (DB), Calinski-Harabaz Index (CH), and ICS. The results show that ITECF outperforms the state-of-the-art algorithms. ITECF obtains eight fine-classifications of CS for the first time, each with different muscle pathological problems in muscle coordination, balance, and strength. And, this fine-classification has the potential for personalized treatment.