Motion artifact synthesis for research in biomedical signal quality analysis

Abstract

Advances in microprocessors, sensor technology, and signal processing have led to the increased feasibility of biosignal-integrated wearable smart devices for long-term monitoring, mobility assistance, and fitness and sports analytics. Motion artifact contamination can adversely affect the interpretation of bioelectric signals (e.g., electrocardiograms, electromyograms) collected by these wearable devices. Research and development of biomedical signal quality analysis algorithms to detect, identify, quantify, and mitigate motion artifact is typically performed using a ground truth signal contaminated with previously recorded or simulated motion artifact. The procedures for generating simulated motion artifact are undercharacterized in the literature, and many research simulations have been based on the same recording of motion artifact data, leading to concerns about the diversity of the simulated data. This paper proposes and compares three methods for synthesizing motion artifact data. Motion artifact data was collected from 5 subjects performing two different tasks (arm movement and walking). Autoregressive (AR), Markov chain (MC), and recurrent neural network (RNN) models were explored to generate simulated motion artifact data. The AR model imitated time and frequency domain properties effectively, but was ineffective for reproducing the morphology and probability distribution. The MC model was able to produce data with similar time domain properties and probability distribution, but was less effective than the RNN model at imitating the frequency domain and morphology. We conclude that the RNN method is promising for synthesizing diverse motion artifact data that resembles the properties of experimental data to achieve a ground truth signal for research in bioelectric signal quality analysis.