A Generative Elastic Network Method for R Peak Detection Suitable for Few-Shot Learning

Abstract

R peak detection is fundamental to the analysis of long-term electrocardiogram (ECG) signals. Despite their significant success in R peak detection, neural networks based on statistical learning usual require more than 50% of all data for training. However, it is often difficult to provide such a high proportion of training data in practice. This paper proposes a novel R peak detection method based on Generative Elastic Network (GEN), which is suitable for few-shot learning. Utilizing the Lobachevsky University Database (LUDB), this method achieves an accuracy exceeding 99% by using less than 3% of the data for training and 14% for validation. It dramatically reduces the dependency on large volumes of data for training and validation, while preserving an accuracy level that is on par with existing methods.

Keywords

• Generative elastic network
• electrocardiogram (ECG)
• R peak detection
• few-shot learning