Abstract
Clinical parameter estimation from the electrocardiogram (ECG) is a
recurrent field of research. It is debated that ECG parameters
estimation by human experts and machines/algorithms is always
model-based (implicitly or explicitly). Therefore, all estimation
algorithms used in this context have performance bounds in terms of the
achievable mean squared error, which are not exceedable. These bounds
depend on the adopted data-model, the estimation scheme (least-squares
error, maximum likelihood, or Bayesian), and prior assumptions on the
model parameters and noise distributions.
In this research, we develop a comprehensive theoretical framework for
ECG parameter estimation and derive the Cramér-Rao lower bounds (CRLBs)
for the most popular signal models used in the ECG modeling literature,
namely functional expansions (including polynomials) and sum of Gaussian
functions. The developed framework is evaluated over real and synthetic
data for three popular applications: T-to-R wave ratio estimation,
ST-segment analysis and QT-interval estimation, using the
state-of-the-art estimators in each context. The proposed framework and
the derived CRLBs provide practical guidelines for the selection of
data-models, sampling frequency (beyond the Nyquist rate), modeling
segment length, number of beats required for ECG beat averaging, and
other factors that influence the accuracy of ECG-based clinical
parameter estimation.