CardiacNet: A Neural Networks Based Heartbeat Classifications using ECG Signals

Obtaining information about the electrical activity of the heart in the form of electrocardiograms (ECG) has become a standard way of monitoring patients’ heart rhythm and function. It is used for diagnosing a variety of cardiac anomalies such as arrhythmia and other heart diseases. However, the interpretation of ECGs requires the expertise of trained physicians, thus bearing the need for tools that automatically classify such signals. In this study we train deep convolutional neural networks (CNNs) to perform binary classification of ECG beats to normal and abnormal. We use transfer learning in order to build models that are fine-tuned on specific patients’ data, after pre-training a generic network on a set of different ECGs selected from the MIT-BIH arrhythmia database. We then compare the performance of the fine-tuned networks against that of individual networks, which are trained only on the ECG data of a single patient, in order to evaluate the overall efficacy of transfer learning on the given problem. We managed to achieve adequate results on both scenarios as the individual classifiers yielded an average of 94.6% balanced accuracy on the test set, whereas the fine-tuned models a marginally worse 93.5%.