  Automatic detection of R-peaks in an Electrocardiogram signal is crucial in a
multitude of applications including Heart Rate Variability (HRV) analysis and
Cardio Vascular Disease(CVD) diagnosis. Although there have been numerous
approaches that have successfully addressed the problem, there has been a
notable dip in the performance of these existing detectors on ECG episodes that
contain noise and HRV Irregulates. On the other hand, Deep Learning(DL) based
methods have shown to be adept at modelling data that contain noise. In image
to image translation, Unet is the fundamental block in many of the networks. In
this work, a novel application of the Unet combined with Inception and Residual
blocks is proposed to perform the extraction of R-peaks from an ECG.
Furthermore, the problem formulation also robustly deals with issues of
variability and sparsity of ECG R-peaks. The proposed network was trained on a
database containing ECG episodes that have CVD and was tested against three
traditional ECG detectors on a validation set. The model achieved an F1 score
of 0.9837, which is a substantial improvement over the other beat detectors.
Furthermore, the model was also evaluated on three other databases. The
proposed network achieved high F1 scores across all datasets which established
its generalizing capacity. Additionally, a thorough analysis of the model's
performance in the presence of different levels of noise was carried out.
