Detection of cervical spondylotic myelopathy based on gait analysis and deterministic learning

Abstract

Cervical spondylotic myelopathy (CSM) is the main cause of cervical spinal cord dysfunction in adults, especially in middle-aged and elderly patients, which easily leads to gait disturbance. In the present study, we propose a dynamic method for the detection of CSM based on nonlinear dynamics of gait system and deterministic learning theory. First, a 3-dimensional (3D) gait analysis system is used to capture the walking locomotion from healthy controls (HCs) and patients with CSM. Discriminant kinematic gait features, including angles of hip and knee joints in the sagittal and coronal planes, are extracted based on statistical analysis and clinicians’ empirical investigation. Second, deterministic learning theory is used to model and identify nonlinear gait system dynamics of HCs and patients with CSM, which are approximated and stored in constant Radial Basis Function (RBF) neural networks (NN). The disparity of gait system dynamics between the two groups of participants is used for classification and detection of the presence of CSM by constructing a bank of dynamic estimators with constant RBF NN. Finally, experiments are carried out on the self-constructed CSM gait database to evaluate the performance of the proposed method, in which gait data from 45 CSM patients and 45 age-matched HCs are involved. By using 2-fold and leave-one-out cross-validation styles, the achieved average classification accuracy is reported to be 94.44$\%$ and 95.56$\%$, respectively. The results demonstrate excellent performance and the proposed method has the potential to serve as a candidate for the automatic detection of CSM in clinical examination.