摘要：Premature ventricular contractions (PVCs) are one of the most common cardiovascular diseases with high risk to a large population of patients. It has been shown that supervised learning algorithms can detect PVCs from beat-level ECG data. However, a huge human effort is needed in order to achieve an accurate detection rate. A convolutional autoencoder was trained in this work in an unsupervised fashion to extract features automatically with zero prior specialized knowledge. Random forest was adopted as a supervised algorithm trained on the features generated by the autoencoder. Various active learning selection strategies, uncertainty-based and diversity-based, were studied on top of the random forest. In each iteration of active learning, the training data are updated with newly selected samples and fed into the classifier. The performance on an independent validation set is recorded in each iteration. As a result, among the different uncertainty sampling strategies, the least confidence score shows a better F1 score of 0.85 than other methods. In between the two diversity-based strategies, the representative clustering sample had the best F1 score than the k-center-greedy algorithm. By comparing the performance of different active learning methods trained on half of the original data size with the same classifier trained on the full set, the F1 score of least confidence is still better than the full set. This study demonstrates that active learning could help reduce human annotation effort by achieving the same level of performance as the classifier trained on the fully annotated training data.
