A linear stochastic state space model for electrocardiograms

Abstract

This paper proposes a linear stochastic state space model for electrocardiogram signal processing and analysis. The model is obtained as a discretized version of Wiener process acceleration model. The model is combined with a fixed-lag Rauch-Tung-Striebel smoother to perform on-line signal denoising, feature extraction, and beat classification. The results indicate that the proposed approach outperforms a conventional FIR filter in terms of improved signal-to-noise ratio, and that the approach can be used for highly accurate online classification of normal beats and premature ventricular contractions. The benefits of the model include the possibility to use closed-form solutions to the optimal filtering and smoothing problems, quick adaptation to sudden changes in beat morphology and heart rate, simple and fast initialization, preprocessing-free operation, intuitive interpretation of the system state, and more.