Application of Tensor Decomposition in Removing Motion Artifacts from the Measurements of a Wireless Electrocardiogram

Wireless electrocardiograms (WECG) facilitate the long-term monitoring of patients in their residential environment. However, the freedom of movement provokes motion artifacts in the measurements of the useful signals, which significantly affect the quality of the data. In this paper, we propose a tensor decomposition method to combine data from heterogeneous sources and remove motion artifacts. We transformed synchronously sampled electrocardiogram and inertial sensors into the time-frequency space using wavelet decomposition. Afterward, we formed a three-way tensor consisting of a single lead WECG and a motion reference. Thus we recorded measurements from eleven healthy subjects undertaking different types of movements, namely, Standing up, Bending forward, Walking, Running, Jumping, and Climbing stairs. An additional WECG sensor was attached at the back of each subject to measure motion with negligible cardiac input. This signal was subsequently added to a noise-free WECG segment to generate artificially corrupted signal. We factorize the measurement sets using Canonical Polyadic Decomposition to determine mutual information present in both sensor types (WECG and inertial sensor) and extract the motion artifacts from the noisy WECG. We evaluated the results by considering the Signal-to-Noise-Ratio and the Root Mean Squared Error between the actual and estimated artifacts.