The QRS complex is a very important part of a heartbeat in the electrocardiogram signal, and it provides useful information for physicians to diagnose heart diseases. Accurately detecting the fiducial points that compose the QRS complex is a challenging task. Another issue concerning the QRS detection is its computational costs since the algorithm should have a fast and real-time response. In this context, there is a trade-off between computational cost and precision. Convolutional networks are a deep learning approach, and it has achieved impressive results in several computer vision and pattern recognition problems. Nowadays there is hardware that fully embeds convolutional network models, significantly reducing computational cost for real-world and real-time applications. In this direction, this work proposes a deep learning approach, based on convolutional network, aiming to detect heartbeat pattern. We tested two different architectures with two different proposes, one very deep and that has small receptive fields, and the other that has larger receptive fields. Preliminary experiments on the MIT-BIH arrhythmia database showed that the studied convolutional network presents promising results for QRS detection which are comparable with state-of-the-art methods.