  Deep-learning based QRS-detection algorithms often require essential
post-processing to refine the prediction streams for R-peak localisation. The
post-processing performs signal-processing tasks from as simple as, removing
isolated 0s or 1s in the prediction-stream to sophisticated steps, which
require domain-specific knowledge, including the minimum threshold of a
QRS-complex extent or R-R interval. Often these thresholds vary among
QRS-detection studies and are empirically determined for the target dataset,
which may have implications if the target dataset differs. Moreover, these
studies, in general, fail to identify the relative strengths of deep-learning
models and post-processing to weigh them appropriately. This study classifies
post-processing, as found in the QRS-detection literature, into two levels -
moderate, and advanced - and advocates that the thresholds be learned by an
appropriate deep-learning module, called a Gated Recurrent Unit (GRU), to avoid
explicitly setting post-processing thresholds. This is done by utilising the
same philosophy of shifting from hand-crafted feature-engineering to
deep-learning-based feature-extraction. The results suggest that GRU learns the
post-processing level and the QRS detection performance using GRU-based
post-processing marginally follows the domain-specific manual post-processing,
without requiring usage of domain-specific threshold parameters. To the best of
our knowledge, the use of GRU to learn QRS-detection post-processing from CNN
model generated prediction streams is the first of its kind. The outcome was
used to recommend a modular design for a QRS-detection system, where the level
of complexity of the CNN model and post-processing can be tuned based on the
deployment environment.
