  The automatic diagnosis of heart diseases from the electrocardiogram (ECG)
signal is crucial in clinical decision-making. However, the use of
computer-based decision rules in clinical practice is still deficient, mainly
due to their complexity and a lack of medical interpretation. The objetive of
this research is to address these issues by providing valuable diagnostic rules
that can be easily implemented in clinical practice. In this research,
efficient diagnostic rules friendly in clinical practice are provided. In this
paper, interesting parameters obtained from the ECG signals analysis are
presented and two simple rules for automatic diagnosis of Bundle Branch Blocks
are defined using new markers derived from the so-called FMMecg delineator. The
main advantages of these markers are the good statistical properties and their
clear interpretation in clinically meaningful terms. High sensitivity and
specificity values have been obtained using the proposed rules with data from
more than 35000 patients from well known benchmarking databases. In particular,
to identify Complete Left Bundle Branch Blocks and differentiate this condition
from subjects without heart diseases, sensitivity and specificity values
ranging from 93% to 99% and from 96% to 99%, respectively. The new markers and
the automatic diagnosis are easily available at
https://fmmmodel.shinyapps.io/fmmEcg/, an app specifically developed for any
given ECG signal. The proposal is different from others in the literature and
it is compelling for three main reasons. On the one hand, the markers have a
concise electrophysiological interpretation. On the other hand, the diagnosis
rules have a very high accuracy. Finally, the markers can be provided by any
device that registers the ECG signal and the automatic diagnosis is made
straightforwardly, in contrast to the black-box and deep learning algorithms.
