Abstract
In this paper, we present a method for modelling an electrocardiogram (ECG) signal using time-varying parameters by considering that the signal is generated by a linear, time-varying (LTV) system with a stationary white noise input, then we estimate the time-varying coefficients of the LTV system. Since the ECG signal is considered to be non-stationary, this method is based on the Wold-Cramer representation of a non-stationary signal. Because the relationship between the generalised transfer function of an LTV system and the time-varying coefficients of the difference equation of a discrete-time system is not addressed so far in literature, in this paper we propose a solution to this problem and apply it for modelling a human ECG signal. We first derive a relationship between the system generalised transfer function and the time-varying parameters of the system. Then we develop an algorithm to solve for the system time-varying parameters from the time-frequency kernel of the system output using the time-varying auto-correlation function (TVACF). A comparison analysis between the proposed algorithm and the RLS and RLSL algorithms has been discussed. Computer simulations illustrating the effectiveness of our algorithm are presented when the signal is embedded in noise.
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