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Automatic detection of R-peaks in an Electrocardiogram signal is crucial in amultitude of applications including Heart Rate Variability (HRV) analysis andCardio Vascular Disease(CVD) diagnosis. Although there have been numerousapproaches that have successfully addressed the problem, there has been anotable dip in the performance of these existing detectors on ECG episodes thatcontain noise and HRV Irregulates. On the other hand, Deep Learning(DL) basedmethods have shown to be adept at modelling data that contain noise. In imageto image translation, Unet is the fundamental block in many of the networks. Inthis work, a novel application of the Unet combined with Inception and Residualblocks is proposed to perform the extraction of R-peaks from an ECG.Furthermore, the problem formulation also robustly deals with issues ofvariability and sparsity of ECG R-peaks. The proposed network was trained on adatabase containing ECG episodes that have CVD and was tested against threetraditional ECG detectors on a validation set. The model achieved an F1 scoreof 0.9837, which is a substantial improvement over the other beat detectors.Furthermore, the model was also evaluated on three other databases. Theproposed network achieved high F1 scores across all datasets which establishedits generalizing capacity. Additionally, a thorough analysis of the model'sperformance in the presence of different levels of noise was carried out.

RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG