# Video-based detection of cessation of breathing in pre-term infants using machine learning

arXiv: https://arxiv.org/abs/2607.05230 · HTML: https://mlsanity.com/paper/2607.05230/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 6 Jul 2026.

## Builds on (most cited first, 7 of 7)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Sparse PCA via Covariance Thresholding](https://mlsanity.com/paper/1311.5179/) (2013)
- [Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality](https://mlsanity.com/paper/2005.05195/) (2020)
- [Sparse Principal Component Analysis via Variable Projection](https://mlsanity.com/paper/1804.00341/) (2018)
- [Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation](https://mlsanity.com/paper/1602.03992/) (2016)
- [Sparse PCA: A New Scalable Estimator Based On Integer Programming](https://mlsanity.com/paper/2109.11142/) (2021)
- [Sparse PCA With Multiple Components](https://mlsanity.com/paper/2209.14790/) (2022)

## Abstract (arXiv)

Pre-term infants are susceptible to potentially harmful apnoea-related cessations of breathing due to immature respiratory control. However, reliable respiratory monitoring in the neonatal intensive care unit (NICU) remains challenging because motion artefacts, sensor displacement, and skin fragility can compromise contact-based measurements. Non-contact video monitoring offers a complementary approach that does not depend on adhesive sensors while providing additional respiratory information. We investigated whether camera-based signals can detect apnoea-related cessation of breathing (COBE) and provide complementary information to routinely acquired physiological signals. Using video and clinical recordings from 30 pre-term infants, respiratory motion was extracted from dynamically tracked torso regions to generate camera-derived time-series signals. Camera-only models were trained using residual network (ResNet) architectures, while hybrid models combined video-derived signals with impedance pneumography (IP), ECG-derived respiration (EDR), and the PPG-derived respiratory envelope. Camera-only models achieved a balanced accuracy of 76.9%, demonstrating the feasibility of non-contact COBE detection. Combining video-derived features with IP improved balanced accuracy to 90.6%, outperforming either modality alone and indicating that video provides respiratory information beyond standard physiological signals. These findings show that video-derived signals contain clinically relevant respiratory features and enhance COBE detection when combined with conventional physiological signals. This supports non-contact video as a complementary modality for automated COBE detection and highlights its potential to improve the robustness of neonatal respiratory monitoring.