A 1-D CNN combined with active learning significantly improves the classification accuracy of ECG signals from wearable devices compared to traditional machine learning methods.
Arrhythmias reflect electrical abnormalities of the heart, and they can lead to severe harm to the heart. An electrocardiogram (ECG) is a useful tool to manifest arrhythmias. In this paper, we present an automatic system using a convolutional neural network and active learning to classify ECG signals. To improve the model performance, breaking-ties (BT) and modified BT algorithms are utilized in the active learning. We classify ECG signals in five heartbeat types, i.e., normal (N), ventricular (V), supraventricular (S), fusion of normal and ventricular (F), and unknown heartbeats (Q), using the Association for the Advancement of Medical Instrumentation standard. Our experiments are performed on the MIT-BIH arrhythmia database. To further verify the generalization capability of the system, the ECG data that acquired from our wearable device are also used to conduct in the experiments. Compared with most of the state-of-the-art methods, the obtained results demonstrate that the presented method promotes the classification performance remarkably.
Xia et al. (Tue,) studied this question.