Scaling Representation Learning from Ubiquitous ECG with State-Space
  Models
Kleanthis Avramidis Dominika Kunc Bartosz Perz Kranti Adsul Tiantian Feng Przemysław Kazienko Stanisław Saganowski Shrikanth Narayanan
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
https://github.com/klean2050/tiles_ecg_model.