Learning Beyond Similarities: Incorporating Dissimilarities between Positive Pairs in Self-Supervised Time Series Learning

TL;DR

This paper introduces DEBS, a self-supervised learning method for time series that incorporates dissimilarities between positive pairs, significantly improving cardiovascular disease detection from ECG signals.

Contribution

It presents a novel SSL framework that leverages dissimilarities among positive pairs, enhancing dynamic feature encoding in time series analysis.

Findings

+10% accuracy in AFib detection

Effective encoding of dynamic characteristics

Potential for advancing SSL in temporal data

Abstract

By identifying similarities between successive inputs, Self-Supervised Learning (SSL) methods for time series analysis have demonstrated their effectiveness in encoding the inherent static characteristics of temporal data. However, an exclusive emphasis on similarities might result in representations that overlook the dynamic attributes critical for modeling cardiovascular diseases within a confined subject cohort. Introducing Distilled Encoding Beyond Similarities (DEBS), this paper pioneers an SSL approach that transcends mere similarities by integrating dissimilarities among positive pairs. The framework is applied to electrocardiogram (ECG) signals, leading to a notable enhancement of +10\% in the detection accuracy of Atrial Fibrillation (AFib) across diverse subjects. DEBS underscores the potential of attaining a more refined representation by encoding the dynamic characteristics of time series data, tapping into dissimilarities during the optimization process. Broadly, the strategy delineated in this study holds the promise of unearthing novel avenues for advancing SSL methodologies tailored to temporal data.