Review of Self-supervised Learning Methods in Field of ECG

Abstract

Deep learning has been widely applied in the field of electrocardiogram (ECG) signal analysis due to its powerful data representation capability. However, supervised methods require a large amount of labeled data, and ECG data annotation is typically time-consuming and costly. Additionally, supervised methods are limited by the finite data types in the training set, resulting in limited generalization performance. Therefore, how to leverage massive unlabeled ECG signals for data mining and universal feature representation has become an urgent problem to be addressed. Self-supervised learning (SSL) is an effective approach to address the issue of missing annotated ECG data and improve the transfer ability of the model by learning generalized features from unlabeled data using pre-defined proxy tasks. However, existing surveys on self-supervised learning mostly focus on the domains of images or temporal signals, and there is a relative lack of comprehensive reviews on self-supervised learning in the ECG domain. To fill this gap, this paper provides a comprehensive review of advanced self-supervised learning methods used in the field of ECG. Firstly, a systematic summary and classification of self-supervised learning methods for ECG are presented, starting from two learning paradigms—contrastive and predictive. The basic principles of different categories of methods are elaborated, and the characteristics of each method are analyzed in detail, highlighting the advantages and limitations of each approach. Subsequently, a summary is provided for the commonly used datasets and application scenarios in ECG self-supervised learning, along with a review of data augmentation methods frequently applied in the ECG domain, offering a systematic reference for subsequent research. Finally, an in-depth discussion is presented on the current challenges of self-supervised learning within the ECG field, and future directions for the development of ECG self-supervised learning are explored, providing guidance for subsequent research in the field.

Keywords

• electrocardiogram (ecg); feature representation; deep learning; self-supervised learning