Simulating Motion Artifact Using an Autoregressive Model for Research in Biomedical Signal Quality Analysis.

Abstract

Motion artifact contamination may adversely affect the interpretation of biological signals. The development of algorithms to detect, identify, quantify, and mitigate motion artifact is typically performed using a ground truth signal contaminated with previously recorded motion artifact, or simulated motion artifact. The diversity of available motion artifact recordings is limited, and the rationales for existing models of motion artifact are poorly described. In this paper we developed an autoregressive (AR) model of motion artifact based on data collected from 6 subjects walking at slow, medium, and fast paces. The AR model was evaluated for its ability to generate diverse data that replicated the properties of the experimental data. The simulated motion artifact data was successful at learning key time domain and frequency domain properties, including the mean, variance, and power spectrum of the data, but was ineffective for imitating the morphology and probability distribution of the motion artifact data (kurtosis % error of 100.9-103.6%). More sophisticated models of motion artifact may be necessary to develop simulations of motion artifact.