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

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.

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Sleep Stage Detection MASS SS3 CatBoost Accuracy 86.7% # 3
Macro-F1 0.817 # 1
Cohen's kappa 0.803 # 1
Sleep Stage Detection Sleep-EDF Linear model Accuracy 86.3% # 4
Macro-F1 0.805 # 4
Cohen's kappa 0.813 # 3
Sleep Stage Detection Sleep-EDF CatBoost Accuracy 86.6% # 2
Macro-F1 0.810 # 2
Cohen's kappa 0.816 # 2
Multimodal Sleep Stage Detection Sleep-EDF-SC Linear model Accuracy 85.7% # 2
Macro-F1 0.809 # 1
Cohen's kappa 0.806 # 2
Multimodal Sleep Stage Detection Sleep-EDF-SC CatBoost Accuracy 86.4% # 1
Macro-F1 0.802 # 2
Cohen's kappa 0.812 # 1
Multimodal Sleep Stage Detection Sleep-EDF-ST Linear model Accuracy 82.9% # 2
Macro-F1 0.792 # 2
Cohen's kappa 0.759 # 2
Multimodal Sleep Stage Detection Sleep-EDF-ST CatBoost Accuracy 83.6% # 1
Macro-F1 0.795 # 1
Cohen's kappa 0.765 # 1


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