Evaluation of the Relevance of Linear and Nonlinear Posturographic Features in the Recognition of Healthy Subjects and Stroke Patients

Since balance control is a basic prerequisite for most of our daily activities, this task has crucial importance in the functional independence of humans. During the balance control, the human body sways constantly, even in the quiet upright stance. This body sway is usually captured in the form of time series of center-of-pressure (COP) displacements with the help of a measurement device known as force platform. In this paper, rather than using the traditional statistical analysis widely found in balance assessment studies, machine learning techniques were employed to recognize stroke patients and healthy matched subjects based on posturographic features extracted from their COP data. In this context, our main purpose was to investigate the relevance of 16 linear and 9 nonlinear posturographic features commonly examined in the balance assessment field. Thus, the average joint performance among six popular classification methods was evaluated under a 65 instances-size dataset in three situations: using only linear features, only nonlinear features and, finally, linear and nonlinear posturographic features combined. The former situation yielded significantly (P <;0.01) better results. This finding suggest that, following an approach based on classification methods to distinguish healthy from stroke physiological systems, the overall amount of sway indexed by the linear features is more relevant than the temporal patterns of sway described by the nonlinear features.