Diagnosis of arrhythmias with few abnormal ECG samples using metric-based meta learning

• Published in: Computers in biology and medicine Vol. 153; p. 106465
• Main Authors: Liu, Zhenxing, Chen, Yujie, Zhang, Yong, Ran, Shaolin, Cheng, Cheng, Yang, Guili
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.02.2023; Elsevier Limited
• Subjects: Algorithms; Arrhythmia; Arrhythmias, Cardiac - diagnosis; Artificial Intelligence; Artificial neural networks; Cardiac arrhythmia; Datasets; Diagnosis; ECG diagnosis; EKG; Electrocardiography; Few shot learning; Heart Rate; Humans; Learning; Localization; Medical diagnosis; Metric meta-learning; Neural Networks, Computer; Resampling; Residual network; Segmentation; Siamese network; Siamese network; ECG diagnosis; Metric meta-learning; Few shot learning; Residual network
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106465

A major challenge in artificial intelligence based ECG diagnosis lies that it is difficult to obtain sufficient annotated training samples for each rhythm type, especially for rare diseases, which makes many approaches fail to achieve the desired performance with limited ECG records. In this paper, we propose a Meta Siamese Network (MSN) based on metric learning to achieve high accuracy for automatic ECG arrhythmias diagnosis with limited ECG records. First, the ECG signals from three different ECG datasets are preprocessed through resampling, wavelet denoising, R-wave localization, heartbeat segmentation and Z-score normalization. Then, an ECG dataset with limited records is constructed to verify the performance of the proposed model and explore variation of model performance with the sample size. Second, a metric-based meta-learning framework is proposed to address the challenge of few-shot learning for automatic ECG diagnosis of cardiac arrhythmia, and siamese network is employed to achieve arrhythmia diagnosis based on similarity metric. Finally, the N-way K-shot meta-testing strategy is proposed based on the siamese network with double inputs, and the experimental results demonstrate that the proposed strategy can effectively improve the robustness of the proposed model. •We propose a few-shot ECG diagnosis framework based on metric meta-learning.•We propose the Meta Siamese Network to implement the heart pairs matching tasks.•ECG datasets with limited records are constructed to verify the performance of our proposed model.