Abstract

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 WildECG, a pre-trained state-space model for representation learning from ECG signals. We train this model in a self …

Metadata

publication

IEEE Journal of Biomedical and Health Informatics, 2024

year

2024

publication date

2024/6/27

authors

Kleanthis Avramidis, Dominika Kunc, Bartosz Perz, Kranti Adsul, Tiantian Feng, Przemysław Kazienko, Stanisław Saganowski, Shrikanth Narayanan

link

https://ieeexplore.ieee.org/abstract/document/10574308/

resource_link

https://ieeexplore.ieee.org/iel8/6221020/6363502/10574308.pdf

journal

IEEE Journal of Biomedical and Health Informatics

publisher

IEEE