Does an active dual-scale residual convolutional LSTM neural network improve heartbeat classification accuracy?
A novel active learning-based deep learning model significantly improves ECG heartbeat classification accuracy while requiring minimal additional labeled data.
In the actual scenario, it is difficult for an automatic diagnosis system to detect each heartbeat when facing new patients. In this article, a novel active dual-scale residual convolutional long short-term memory neural network is proposed to classify heartbeats interactively and automatically. First, each heartbeat and electrocardiogram segment including current heartbeat are combined to build dual-scale input information. In addition, convolution layers, residual block, and bidirectional long short memory are integrated to extract features from dual-scale inputs. Furthermore, active learning approach with random breaking-ties selection strategy is introduced to choose the most representative unlabeled samples for labeling and fine-tune the trained model with these representative labeled samples. The experimental results show that the proposed method uses only 5.14% of the additional data set and improves the model accuracy and macro F1 score by 13.23% and 35.62%, respectively. The proposed structure has good representative ability and our selection strategy can reduce the workload of experts and improve the performance greatly. Thus, the proposed interactive and incremental method has good potential in real applications.
Jin et al. (Fri,) studied this question.