# Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring

arXiv: https://arxiv.org/abs/2207.07753 · HTML: https://mlsanity.com/paper/2207.07753/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 66 references that are in the map.
- First posted on arXiv 15 Jul 2022.

## Builds on (most cited first, 20 of 20)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging](https://mlsanity.com/paper/2007.05492/) (2020)
- [SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification](https://mlsanity.com/paper/2105.11043/) (2021)
- [A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series](https://mlsanity.com/paper/1707.03321/) (2017)
- [SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach](https://mlsanity.com/paper/1903.02108/) (2019)
- [Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification](https://mlsanity.com/paper/1805.06546/) (2018)
- [Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy](https://mlsanity.com/paper/1710.02094/) (2017)
- [Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging](https://mlsanity.com/paper/1911.03221/) (2019)
- [Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning](https://mlsanity.com/paper/1907.13177/) (2019)
- [Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations](https://mlsanity.com/paper/2006.01667/) (2020)
- [DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates](https://mlsanity.com/paper/2108.10600/) (2021)
- [RobustSleepNet: Transfer learning for automated sleep staging at scale](https://mlsanity.com/paper/2101.02452/) (2021)
- [Mixed Neural Network Approach for Temporal Sleep Stage Classification](https://mlsanity.com/paper/1610.06421/) (2016)
- [Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks](https://mlsanity.com/paper/1610.01683/) (2016)
- [Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring](https://mlsanity.com/paper/1710.00633/) (2017)
- [tsflex: flexible time series processing & feature extraction](https://mlsanity.com/paper/2111.12429/) (2021)
- [Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms](https://mlsanity.com/paper/1810.03745/) (2018)
- [Plotly-Resampler: Effective Visual Analytics for Large Time Series](https://mlsanity.com/paper/2206.08703/) (2022)

## Built on by (most cited first, 7 of 7)

- [ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation](https://mlsanity.com/paper/2212.04881/) (2022)
- [Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification](https://mlsanity.com/paper/2303.17809/) (2023)
- [A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input](https://mlsanity.com/paper/2408.15253/) (2024)
- [Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death](https://mlsanity.com/paper/2208.12814/) (2022)
- [Left-Right Swapping and Upper-Lower Limb Pairing for Robust Multi-Wearable Workout Activity Detection](https://mlsanity.com/paper/2408.03947/) (2024)
- [Data-Efficient Sleep Staging with Synthetic Time Series Pretraining](https://mlsanity.com/paper/2403.08592/) (2024)
- [Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data Modalities](https://mlsanity.com/paper/2407.11048/) (2024)

## Abstract (arXiv)

Over the last few years, research in automatic sleep scoring has mainly focused on developing increasingly complex deep learning architectures. However, recently these approaches achieved only marginal improvements, often at the expense of requiring more data and more expensive training procedures. Despite all these efforts and their satisfactory performance, automatic sleep staging solutions are not widely adopted in a clinical context yet. We argue that most deep learning solutions for sleep scoring are limited in their real-world applicability as they are hard to train, deploy, and reproduce. Moreover, these solutions lack interpretability and transparency, which are often key to increase adoption rates. In this work, we revisit the problem of sleep stage classification using classical machine learning. Results show that competitive performance can be achieved with a conventional machine learning pipeline consisting of preprocessing, feature extraction, and a simple machine learning model. In particular, we analyze the performance of a linear model and a non-linear (gradient boosting) model. Our approach surpasses state-of-the-art (that uses the same data) on two public datasets: Sleep-EDF SC-20 (MF1 0.810) and Sleep-EDF ST (MF1 0.795), while achieving competitive results on Sleep-EDF SC-78 (MF1 0.775) and MASS SS3 (MF1 0.817). We show that, for the sleep stage scoring task, the expressiveness of an engineered feature vector is on par with the internally learned representations of deep learning models. This observation opens the door to clinical adoption, as a representative feature vector allows to leverage both the interpretability and successful track record of traditional machine learning models.