A recurrent neural network (RNN) is presented in this work for automatic detection of atrial fibrillation from raw ECG signals without any hand-crafted feature extraction. We designed a stacked long-short term memory (LSTM) network - a special RNN with capability of learning long-term temporal dependencies in the ECG signal. The RNN is digitally synthesized in 65nm CMOS process, and consumes 21.8nJ/inference at 1kHz operating frequency, while achieving state-of-the-art classification accuracy of 85.7% and f1-score of 0.82. The energy consumption of the proposed RNN is 8 χ lower than state-of-the-art integrated circuits for arrhythmia detection.