In this paper, a pre-trained neural network was implemented for detecting the QRS feature of an ECG signal, which is crucial for auto-diagnostic of various cardiopathies. To take advantage of the fast evolution of artificial intelligence and its ability to find non-linear relationships, NN based feature extraction of ECG signals for wearable devices was explored and tested using ASIC implementation flow. RNN was created and trained using the data obtained from PhysioNET database. A high-level performance evaluation was carried out for P and T wave extraction. An accuracy of 96.55% was achieved in the hardware implementation of the network.