Non-Iterative Missing Samples Recovery of ECG Signals by Lmmse Estimation for an Autoregressive Cyclostationary Model

Electrocardiography (ECG) measured using wearable wireless sensors is already commonly used for several years, as one of the products of the emerging Telemedicine field, which is one the main branches in eHealth applications. In this work we address the problem of missing samples recovery of such ECG (digital) signals, resulting from temporally-local communication dropouts. We propose a new model for the ECG signal based on its conspicuous quasi-periodical characteristics in short time intervals, along with a compatible estimation procedure tailored to the proposed model. We extend the autoregressive (AR) model, previously proposed by Prieto-Guerrero et al., to a cyclostationary AR model, and our proposed estimation scheme incorporates a first phase of model parameters estimation, followed by a Linear Minimum Mean Squared Error (LMMSE) estimation phase of the missing samples. We demonstrate significant improvement compared to the AR method in simulation experiments using real ECG data.