  It is challenging to visually detect heart disease from the
electrocardiographic (ECG) signals. Implementing an automated ECG signal
detection system can help diagnosis arrhythmia in order to improve the accuracy
of diagnosis. In this paper, we proposed, implemented, and compared an
automated system using two different frameworks of the combination of
convolutional neural network (CNN) and long-short term memory (LSTM) for
classifying normal sinus signals, atrial fibrillation, and other noisy signals.
The dataset we used is from the MIT-BIT Arrhythmia Physionet. Our approach
demonstrated that the cascade of two deep learning network has higher
performance than the concatenation of them, achieving a weighted f1 score of
0.82. The experimental results have successfully validated that the cascade of
CNN and LSTM can achieve satisfactory performance on discriminating ECG
signals.
