A Novel Method for Detection of ECG with Deep Learning

With the rapid development of machine learning and deep learning, ECG intelligent detection models lean more heavily on the labeled data. However, the development of ECG annotation cannot satisfy the development of an intelligent detection model because of its high complexity and heavy workload. Developing a model with low dependence on labeled data becomes the core for solving the ECG intelligent detection. To overcome this limitation, this paper proposes a deep active learning (DAL) model for ECG intelligent detection, which combines high-performance deep learning (DL) with active learning (AL) that is less dependent on labeled data. Experimental results show that the proposed method can achieve high performance with the support of a small amount of labeled data. The proposed method could be adopted for data annotation, which achieves a high-quality data annotation and avoids useless annotation.