Key result
Proposed algorithm reduces computational cost for ECG signal modeling by transforming nonlinear to linear fitting.
Why the study?
Nonlinear least-squares fitting of a Gaussian signal on a polynomial background is computationally costly and requires initial parameter estimates.
A novel linear least-squares fitting algorithm improves the computational efficiency and robustness of ECG signal modeling and RS-amplitude estimation.
Simplifies Gaussian fitting workflows; leaves open validation across diverse real-world datasets.
In this letter, an efficient algorithm is presented for fitting a Gaussian signal riding on a polynomial background. It is shown that the nonlinear least-squares fitting can be transformed into a standard linear least-squares fitting. The proposed method has the advantage of not requiring the initial estimates of the parameters, and it significantly reduces the computational cost. Various applications of this method have been successfully applied to real world problems; including the problem of estimating the parameters of characteristic waveforms on the modeling of electrocardiogram (ECG) signals and the problem of robust ECG RS-amplitude estimation.
No takes yet. Share an insight, caveat, or question.
Arman Kheirati Roonizi (2013) conducted a letter in ECG signal modeling. Algorithm for fitting a Gaussian signal riding on a polynomial background was evaluated. A proposed algorithm for fitting a Gaussian signal on a polynomial background transformed nonlinear to linear least-squares fitting, reducing computational cost for ECG signal modeling.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: