ECG signal compression plus noise filtering with truncated orthogonal expansions

With the increasing use of the electrocardiographic signal (ECG) as a diagn ostic tool in cardiology, there exists a requirement for effective ECG comp ression techniques. The goal of any data compression system is to maximize compression while minimizing distortion. Orthogonal expansions is a tool wi dely used because of its compression capacity in recurrent signals. In this paper we analyze the effect of noise in orthogonal expansions of ECG signa ls. When the observed signal is embedded in additive noise, distortion meas urements, such as the mean-square error, are not a monotonic decreasing fun ction of the number of transform coefficients, due to the noise presence. W e analyze and compare two different ways to estimate the transform coeffici ents: inner product and adaptive estimation with the LMS algorithm. For sta tionary signals, we demonstrate and quantify the superior performance obtai ned by the adaptive system when low values of the step-size are used mu < m u(lim), For non-stationary signals, we propose, based on experimental resul ts, values of the LMS step-size mu depending on the noise characteristics a nd the signal-to-noise ratio. Theoretical results are contrasted with a sim ulation study with actual ECG signals from MIT-BIH Arrythmia database and t hree kinds of noise: simulated Gaussian white noise, and two records of phy siological noise that essentially contains electrode motion artifacts and m uscular activity. (C) 1999 Elsevier Science B.V. All rights reserved.