Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory Queue

TL;DR

This paper introduces a novel contrastive learning-based ECG pretrained model that incorporates a patient memory queue and data augmentation techniques to improve robustness and performance with limited labeled data.

Contribution

It proposes the Patient Memory Queue (PMQ) to better exploit patient consistency and introduces two data augmentation methods, advancing ECG pretraining with contrastive learning.

Findings

Outperforms previous contrastive learning methods on public datasets.

Shows increased robustness in limited labeled data scenarios.

Demonstrates effectiveness of patient memory queue and data augmentation.

Abstract

In the field of automatic Electrocardiogram (ECG) diagnosis, due to the relatively limited amount of labeled data, how to build a robust ECG pretrained model based on unlabeled data is a key area of focus for researchers. Recent advancements in contrastive learning-based ECG pretrained models highlight the potential of exploiting the additional patient-level self-supervisory signals inherent in ECG. They are referred to as patient contrastive learning. Its rationale is that multiple physical recordings from the same patient may share commonalities, termed patient consistency, so redefining positive and negative pairs in contrastive learning as intrapatient and inter-patient samples provides more shared context to learn an effective representation. However, these methods still fail to efficiently exploit patient consistency due to the insufficient amount of intra-inter patient samples existing in a batch. Hence, we propose a contrastive learning-based ECG pretrained model enhanced by the Patient Memory Queue (PMQ), which incorporates a large patient memory queue to mitigate model degeneration that can arise from insufficient intra-inter patient samples. In order to further enhance the performance of the pretrained model, we introduce two extra data augmentation methods to provide more perspectives of positive and negative pairs for pretraining. Extensive experiments were conducted on three public datasets with three different data ratios. The experimental results show that the comprehensive performance of our method outperforms previous contrastive learning methods and exhibits greater robustness in scenarios with limited labeled data. The code is available at https://github.com/3hiuwoo/PMQ.