A robust deep convolutional neural network with batch-weighted loss for heartbeat classification

The early detection of abnormal heart rhythm has become crucial due to the spike in the rate of deaths caused by cardiovascular diseases. While many existing works tried to classify heartbeats accurately, they suffered from the imbalance between heartbeat classes in the available ECG datasets since abnormal heartbeats appear much less frequently than normal ones. In addition, most of existing methods heavily rely on data preprocessing such as noise removal and feature extraction, which is computationally expensive, thus limits their use on low-cost portable ECG devices. We present a novel deep convolutional neural network based on state-of-the-art deep learning techniques for accurate heartbeat classification. We suggest a batch-weighted loss function to better quantify the loss in order to overcome the imbalance between classes. The loss weights dynamically change as the distribution of classes in each batch changes. Also, we propose to use multiple heartbeats for more effective heartbeat classification. Even though we use ECG signal from one lead only without any data preprocessing, our method consistently outperforms existing methods of 5-class heartbeat classification. Our accuracy, positive productivity, sensitivity and specificity under intra-patient paradigm are 99.48%, 98.83%, 96.97% and 99.87%, and those under inter-patient paradigm are 88.34%, 48.25%, 90.90% and 88.51% respectively.