  Obtaining per-beat information is a key task in the analysis of cardiac
electrocardiograms (ECG), as many downstream diagnosis tasks are dependent on
ECG-based measurements. Those measurements, however, are costly to produce,
especially in recordings that change throughout long periods of time. However,
existing annotated databases for ECG delineation are small, being insufficient
in size and in the array of pathological conditions they represent. This
article delves has two main contributions. First, a pseudo-synthetic data
generation algorithm was developed, based in probabilistically composing ECG
traces given "pools" of fundamental segments, as cropped from the original
databases, and a set of rules for their arrangement into coherent synthetic
traces. The generation of conditions is controlled by imposing expert knowledge
on the generated trace, which increases the input variability for training the
model. Second, two novel segmentation-based loss functions have been developed,
which attempt at enforcing the prediction of an exact number of independent
structures and at producing closer segmentation boundaries by focusing on a
reduced number of samples. The best performing model obtained an $F_1$-score of
99.38\% and a delineation error of $2.19 \pm 17.73$ ms and $4.45 \pm 18.32$ ms
for all wave's fiducials (onsets and offsets, respectively), as averaged across
the P, QRS and T waves for three distinct freely available databases. The
excellent results were obtained despite the heterogeneous characteristics of
the tested databases, in terms of lead configurations (Holter, 12-lead),
sampling frequencies ($250$, $500$ and $2,000$ Hz) and represented
pathophysiologies (e.g., different types of arrhythmias, sinus rhythm with
structural heart disease), hinting at its generalization capabilities, while
outperforming current state-of-the-art delineation approaches.
