A linear system model for the averaging process of cardiac signals demonstrated that alignment errors can both add and subtract signal components, allowing for the estimation and minimization of signal distortion.
The averaging process is modeled as a linear system whose low-pass filter characteristics are determined by the degree in temporal misalignment of signals. Assuming the errors in temporal alignment of successive cardiac cycles are random, then the model transfer function is equivalent to the probability density function. The response of the model to a step input is equivalent to the probability distribution function, which can be readily quantified. To validate the model, a high resolution ECG amplifier and QRS recognition system was constructed that synchronizes a step input with a point on the QRS. Design criteria for optimal amplification, filtering, and triggering of the ECG are determined. Test of the model reveals a close correspondence between observed and predicted step responses. From the average step response, the recording fidelity of any average can be determined-rapidly while the alignment is adjusted for optimal precision. Using ECG signals from patients, our model system demonstrates that alignment errors can both add and subtract signal components. Methods for estimating the extent of signal distortion induced by averaging as well as criteria for minimizing it are presented.
Craelius et al. (Wed,) reported a other. High resolution ECG amplifier and QRS recognition system was evaluated on Recording fidelity and signal distortion. A linear system model for the averaging process of cardiac signals demonstrated that alignment errors can both add and subtract signal components, allowing for the estimation and minimization of signal distortion.