# Multimodal Sleep Apnea Detection with Missing or Noisy Modalities

arXiv: https://arxiv.org/abs/2402.17788 · HTML: https://mlsanity.com/paper/2402.17788/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 69 references that are in the map.
- First posted on arXiv 24 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [Gated Multimodal Units for Information Fusion](https://mlsanity.com/paper/1702.01992/) (2017)
- [Gated Fusion Network for Single Image Dehazing](https://mlsanity.com/paper/1804.00213/) (2018)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [Towards Good Practices for Missing Modality Robust Action Recognition](https://mlsanity.com/paper/2211.13916/) (2022)
- [AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decoder](https://mlsanity.com/paper/2203.10808/) (2022)
- [A Large Collection of Real-world Pediatric Sleep Studies](https://mlsanity.com/paper/2102.13284/) (2021)
- [Gated Fusion Network for Joint Image Deblurring and Super-Resolution](https://mlsanity.com/paper/1807.10806/) (2018)
- [Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition](https://mlsanity.com/paper/2011.04249/) (2020)
- [Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives](https://mlsanity.com/paper/2111.04898/) (2021)
- [Sensor Fusion using Backward Shortcut Connections for Sleep Apnea Detection in Multi-Modal Data](https://mlsanity.com/paper/1912.06879/) (2019)
- [SomnNET: An SpO2 Based Deep Learning Network for Sleep Apnea Detection in Smartwatches](https://mlsanity.com/paper/2108.11468/) (2021)

## Built on by (most cited first, 2 of 2)

- [KindSleep: Knowledge-Informed Diagnosis of Obstructive Sleep Apnea from Oximetry](https://mlsanity.com/paper/2603.04755/) (2026)
- [Deep learning-based detection of cessation of breathing in pre-term infants](https://mlsanity.com/paper/2606.23213/) (2026)

## Abstract (arXiv)

Polysomnography (PSG) is a type of sleep study that records multimodal physiological signals and is widely used for purposes such as sleep staging and respiratory event detection. Conventional machine learning methods assume that each sleep study is associated with a fixed set of observed modalities and that all modalities are available for each sample. However, noisy and missing modalities are a common issue in real-world clinical settings. In this study, we propose a comprehensive pipeline aiming to compensate for the missing or noisy modalities when performing sleep apnea detection. Unlike other existing studies, our proposed model works with any combination of available modalities. Our experiments show that the proposed model outperforms other state-of-the-art approaches in sleep apnea detection using various subsets of available data and different levels of noise, and maintains its high performance (AUROC>0.9) even in the presence of high levels of noise or missingness. This is especially relevant in settings where the level of noise and missingness is high (such as pediatric or outside-of-clinic scenarios).