# Wearable ECG Quality Assessment: A Deep Learning and Ambulatory Context-Awareness Approach

arXiv: https://arxiv.org/abs/2609.29396 · HTML: https://mlsanity.com/paper/2609.29396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 12 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Abstract (arXiv)

This paper presents and evaluates a Deep Learning-based (DL-based) Signal Quality Assessment (SQA) model to distinguish between clean and noisy ambulatory Electrocardiograms (ECG). The model is trained on Copenhagen Center for Health Technology-Contextualized Arrhythmia Database (CACHET-CADB), which, to the best of our knowledge, is the first ambulatory ECG database with both physical and patient-reported contextual data. The model shows stable performance on different databases such as MIT-databases and the latest PyhsioNet/Cinc Challenge 2021 databases. Subsequently, the paper demonstrates how complicated ECG noise can be investigated by the SQA model and the physical contextual data.