A One-Dimensional Siamese Few-Shot Learning Approach for ECG Classification under Limited Data
Zongjin Li, Huan Wang, Xinwen Liu
Abstract
Electrocardiogram (ECG) is mainly used by medical domain to diagnose arrhythmia. With the development of deep learning algorithms in the ECG classification field, related algorithms have achieved very high accuracy. However, the training of deep learning algorithms always requires large amounts of samples, while the labeled samples are often lacked in the field of medical signals. Therefore, the performance of deep learning algorithms will be greatly restricted. To overcome the sample scarcity problem, we propose a few-shot ECG classification approach based on the Siamese network. This network architecture first uses two one-dimensional convolutional neural network (CNN) that share weights to extract feature vectors of the paired input signals. Then, L1-distance between the two feature vectors is calculated and inputted into the fully connected layer with an activation function sigmoid to determine whether the input pairs belong to same category. We validated our method on the MIT-BIH arrhythmia database. By experiments, our method performs better than existing networks under the circumstance of extremely few amounts of data.