# Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms

arXiv: https://arxiv.org/abs/1810.03745 · HTML: https://mlsanity.com/paper/1810.03745/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 5 of 5)

- [DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates](https://mlsanity.com/paper/2108.10600/) (2021)
- [U-Sleep's resilience to AASM guidelines](https://mlsanity.com/paper/2209.11173/) (2022)
- [Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring](https://mlsanity.com/paper/2207.07753/) (2022)
- [MSED: a multi-modal sleep event detection model for clinical sleep analysis](https://mlsanity.com/paper/2101.02530/) (2021)
- [Sleep Staging Based on Multi Scale Dual Attention Network](https://mlsanity.com/paper/2107.08442/) (2021)

## Abstract (arXiv)

We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use.