Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring

Published in

Biomedical Signal Processing and Control(2022)

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TL;DR

It is shown 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, which opens the door to clinical adoption.

Authors

Jeroen Van Der Donckt

1 papers

Jonas Van Der Donckt

1 papers

Emiel Deprost

1 papers

N. Vandenbussche

1 papers

Michael Rademaker

1 papers

Gilles Vandewiele

1 papers

Sofie van Hoecke

1 papers

References66 items

Plotly-Resampler: Effective Visual Analytics for Large Time Series

Beyond traditional sleep scoring: Massive feature extraction and data-driven clustering of sleep time series.

Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features

Tsflex: Flexible Time Series Processing & Feature Extraction

An open-source, high-performance tool for automated sleep staging

Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring

Published in

Biomedical Signal Processing and Control(2022)

External Links:

Generate Graph DownloadPDF

TL;DR

Authors

Jeroen Van Der Donckt

1 papers

Jonas Van Der Donckt

1 papers

Emiel Deprost

1 papers

N. Vandenbussche

1 papers

Michael Rademaker

1 papers

Gilles Vandewiele

1 papers

Sofie van Hoecke

1 papers

References66 items

Plotly-Resampler: Effective Visual Analytics for Large Time Series

Beyond traditional sleep scoring: Massive feature extraction and data-driven clustering of sleep time series.

Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features

Tsflex: Flexible Time Series Processing & Feature Extraction

An open-source, high-performance tool for automated sleep staging

DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model With Uncertainty Estimates

SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification

TL;DR

Authors

References66 items

Plotly-Resampler: Effective Visual Analytics for Large Time Series

Beyond traditional sleep scoring: Massive feature extraction and data-driven clustering of sleep time series.

Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features

Tsflex: Flexible Time Series Processing & Feature Extraction

An open-source, high-performance tool for automated sleep staging

TL;DR

Authors

References66 items

Plotly-Resampler: Effective Visual Analytics for Large Time Series

Beyond traditional sleep scoring: Massive feature extraction and data-driven clustering of sleep time series.

Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features

Tsflex: Flexible Time Series Processing & Feature Extraction

An open-source, high-performance tool for automated sleep staging

DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model With Uncertainty Estimates

SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification

An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

U-Sleep: resilient high-frequency sleep staging

STQS: Interpretable multi-modal Spatial-Temporal-seQuential model for automatic Sleep scoring

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Intra- and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG

XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging

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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

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National institutes of health sleep disorders research plan

Log loss is not included in the comparison table, as this metric was mostly not reported in other comparable work

LFS : The model is trained from scratch on the current dataset and evaluated with a cross-validation procedure on the evaluation dataset

Additional experiments investigating the impact of various input (subset) combinations on performance

Do Not Sleep on Traditional Machine Learning 17

a t-SNE projection of the feature vector

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