A New Approach to Gaussian Signal Smoothing: Application to ECG Components Separation

An efficient smoothing algorithm is presented in this letter. In this algorithm, while the signal is represented by sum of Gaussian functions-based dynamical model, its states are estimated using a constrained optimization problem where the Gaussian recurrence relation enters as constrain. The accuracy of the proposed method depends on the proper choice of regularization parameters. The value of the parameters is obtained using L-curve, U-curve, or V-curve method. As an application, the method is used for decomposing electrocardiogram (ECG) signal into its components waveform.