  Deep Learning (DL) methods have been used for electrocardiogram (ECG)
processing in a wide variety of tasks, demonstrating good performance compared
with traditional signal processing algorithms. These methods offer an efficient
framework with a limited need for apriori data pre-processing and feature
engineering. While several studies use this approach for ECG signal
delineation, a significant gap persists between the expected and the actual
outcome. Existing methods rely on a sample-to-sample classifier. However, the
clinical expected outcome consists of a set of onset, offset, and peak for the
different waves that compose each R-R interval. To align the actual with the
expected output, it is necessary to incorporate post-processing algorithms.
This counteracts two of the main advantages of DL models, since these
algorithms are based on assumptions and slow down the method's performance. In
this paper, we present Keypoint Estimation for Electrocardiogram Delineation
(KEED), a novel DL model designed for keypoint estimation, which organically
offers an output aligned with clinical expectations. By standing apart from the
conventional sample-to-sample classifier, we achieve two benefits: (i)
Eliminate the need for additional post-processing, and (ii) Establish a
flexible framework that allows the adjustment of the threshold value
considering the sensitivity-specificity tradeoff regarding the particular
clinical requirements. The proposed method's performance is compared with
state-of-the-art (SOTA) signal processing methods. Remarkably, KEED
significantly outperforms despite being optimized with an extremely limited
annotated data. In addition, KEED decreases the inference time by a factor
ranging from 52x to 703x.
