Ubiquitous sensing from wearable devices in the wild holds promise for
enhancing human well-being, from diagnosing clinical conditions and measuring
stress to building adaptive health promoting scaffolds. But the large volumes
of data therein across heterogeneous contexts pose challenges for conventional
supervised learning approaches. Representation Learning from biological signals
is an emerging realm catalyzed by the recent advances in computational modeling
and the abundance of publicly shared databases. The electrocardiogram (ECG) is
the primary researched modality in this context, with applications in health
monitoring, stress and affect estimation. Yet, most studies are limited by
small-scale controlled data collection and over-parameterized architecture
choices. We introduce \textbf{WildECG}, a pre-trained state-space model for
representation learning from ECG signals. We train this model in a
self-supervised manner with 275,000 10s ECG recordings collected in the wild
and evaluate it on a range of downstream tasks. The proposed model is a robust
backbone for ECG analysis, providing competitive performance on most of the
tasks considered, while demonstrating efficacy in low-resource regimes. The
code and pre-trained weights are shared publicly at
this https URL

利用佩戴设备进行普遍感知在提升人类福祉方面具有潜力，从诊断临床状况和测量压力到构建适应性的促进健康的框架。然而，异质背景下的海量数据对传统的监督学习方法提出了挑战。生物信号的表示学习是一个新兴的领域，受到计算建模的最新进展和公开共享数据库的丰富影响。心电图（ECG）是这一领域的主要研究模态，具有健康监测、压力和情感估计等应用。我们引入了一种名为 WildECG 的预训练状态空间模型，用于从 ECG 信号进行表示学习。我们以自我监督的方式训练了这个模型，使用了从野外收集的 275,000 个 10 秒的 ECG 记录，并对各种下游任务进行评估。该模型对于 ECG 分析是一个稳健的基础架构，在大多数考虑到的任务中表现出竞争力，并且在资源有限的情况下显示出有效性。代码和预训练权重已公开共享在指定的 URL 网址。