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The electrocardiogram signal (ECG), a record of electrical activity of the cardiac muscle, has been used in diagnosing many cardiopathies. Wearable devices equipped with readout sensors and circuits can be used to record and process weak ECG signals. 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, neural network based feature extraction of ECG signals for wearable devices was explored and tested using ASIC implementation flow. Firstly, a high-level simulation was carried out in MATLAB and verified with test data obtained from PhysioNET database. Recurrent neural network (RNN) MLP was created and trained using the data obtained from PhysioNET database. A high-level performance evaluation was carried out using the same network for P and T wave extraction. The weight and bias matrices obtained from the high-level trained network in MATLAB were used in the design of the hardware. An accuracy of 96.55% was achieved in the hardware implementation of the network.

View paper and presentation: https://ieeexplore-ieee-org.libconnect.ku.ac.ae/document/9180703/metrics#metrics