Hilbert-Huang-based tremor removal to assess postural properties from accelerometers.

Abstract

Tremor is one of the symptoms of several disorders of the central and peripheral nervous system, such as Parkinson's disease (PD). The impairment of postural control is another symptom of PD. The conventional method of posture analysis uses force plates, but accelerometers can be a valid and reliable alternative. Both these measurement techniques are sensitive to tremor. Tremor affects postural measures and may thus lead to misleading results or interpretations. Linear low-pass filters (LPFs) are commonly employed for tremor removal. In this study, an alternative method, based on Hilbert-Huang transformation (HHT), is proposed. We examined 20 PD subjects, with and without tremor, and 20 control subjects. We compared the effectiveness of LPF and HHT-based filtering on a set of postural parameters extracted from acceleration signals. HHT has the advantage of providing a filter, which with no a priori knowledge, efficiently manages the nonlinear, nonstationary interference due to tremor, and beyond tremor, gives descriptive measures of postural function. Some of the differences found using LPF can instead be ascribed to inefficient noise/tremor suppression. Filter order and cutoff frequency are indeed critical when subjects exhibit a tremorous behavior, in which case LPF parameters should be chosen very carefully.