Automated Detection of Atrial Fibrillation using Deep Learning Techniques

Cardiac arrhythmia occurs when electric impulses co-ordinating the heart beat malfunction. In atrial fibrillation (AF), heart beats irregularly, which increases the risk of stroke and heart diseases. In this paper, the authors apply deep learning techniques to categorize ECG to classes of normal, AF or others. They utilize convolutional neural network (CNN) and hybrid of CNN and other deep learning architectures of long short-term memory (LSTM), gated recurrent unit (GRU) and recurrent neural network (RNN) to automatically detect AF. No feature extraction/selection is required. A number of trials of experiments were run (1000 epochs) to arrive at the optimum value of parameters. Learning rate was fixed in the range [0.01- 0.5]. A high accuracy of 83.5% is obtained using separate training and testing datasets in classifying the input ECG as belonging to normal, abnormal (atrial fibrillation) and others with CNN-LSTM. This is the first work to perform classification mainly to detect AF using ECG recordings of very small duration (average 30s) with high accuracy employing deep learning techniques.