提出新方法同时学习心电图的恒定与动态特征，提升健康监测精度。
Parallel-Learning of Invariant and Tempo-variant Attributes of Single-Lead Cardiac Signals: PLITA
- 并行学习心电图中不变与随时间变化的特征
- 在动态特征主导的任务中显著优于现有方法
- 适合可穿戴设备的心电分析场景
可穿戴传感设备（如霍尔特监测仪）将在数字健康中发挥关键作用。自监督学习（SSL）框架对将单导联心电图（ECG）信号与其临床结果关联至关重要。这些信号具有随时间演变的时变成分和保持不变的恒定成分。然而，现有SSL方法仅关注编码恒定属性，忽略了反映个体状态随时间变化的时变信息。本文提出并行学习恒定与时变属性（PLITA）的新方法，通过强制时间上相近的输入在表示空间中也更接近，来捕捉时变特征。我们评估了该方法在学习两类属性方面的能力，以及在心电分析中相比现有方法的表现。结果显示，在时变属性起主导作用的设置中，PLITA表现显著更优。
原文摘要 · Abstract (English)
Wearable sensing devices, such as Holter monitors, will play a crucial role in the future of digital health. Unsupervised learning frameworks such as Self-Supervised Learning (SSL) are essential to map these single-lead electrocardiogram (ECG) signals with their anticipated clinical outcomes. These signals are characterized by a tempo-variant component whose patterns evolve through the recording and an invariant component with patterns that remain unchanged. However, existing SSL methods only drive the model to encode the invariant attributes, leading the model to neglect tempo-variant information which reflects subject-state changes through time. In this paper, we present Parallel-Learning of Invariant and Tempo-variant Attributes (PLITA), a novel SSL method designed for capturing both invariant and tempo-variant ECG attributes. The latter are captured by mandating closer representations in space for closer inputs on time. We evaluate both the capability of the method to learn the attributes of these two distinct kinds, as well as PLITA's performance compared to existing SSL methods for ECG analysis. PLITA performs significantly better in the set-ups where tempo-variant attributes play a major role.
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