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.

翻译：通过识别连续输入之间的相似性，用于时间序列分析的自监督学习方法在编码时间数据固有静态特征方面展现了有效性。然而，仅强调相似性可能导致表征忽略了在有限受试者队列中建模心血管疾病至关重要的动态属性。本文提出"超越相似性的蒸馏编码"（DEBS），开创了一种自监督学习方法，通过融合正样本对之间的差异来突破仅依赖相似性的局限。该框架应用于心电图信号，使得跨不同受试者的心房颤动检测准确率显著提升10%。DEBS通过在优化过程中利用差异来编码时间序列数据的动态特征，凸显了获得更精细表征的潜力。广义而言，本研究阐述的策略有望为推进面向时序数据的自监督学习方法开辟新途径。