A1 Journal article (refereed)
Automatic sleep scoring : a deep learning architecture for multi-modality time series (2021)

Yan, R., Li, F., Zhou, D.D., Ristaniemi, T., & Cong, F. (2021). Automatic sleep scoring : a deep learning architecture for multi-modality time series. Journal of Neuroscience Methods, 348, Article 108971. https://doi.org/10.1016/j.jneumeth.2020.108971

JYU authors or editors

Publication details

All authors or editorsYan, Rui; Li, Fan; Zhou, Dong Dong; Ristaniemi, Tapani; Cong, Fengyu

Journal or seriesJournal of Neuroscience Methods



Publication year2021


Article number108971


Publication countryNetherlands

Publication languageEnglish


Publication open accessNot open

Publication channel open access

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/73831


Background: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning architecture to automate sleep scoring using raw polysomnography recordings.
Method: The model adopts a linear function to address different numbers of inputs, thereby extending model applications. Two-dimensional convolution neural networks are used to learn features from multi-modality polysomnographic signals, a “squeeze and excitation” block to recalibrate channel-wise features, together with a long short-term memory module to exploit long-range contextual relation. The learnt features are finally fed to the decision layer to generate predictions for sleep stages.
Result: Model performance is evaluated on three public datasets. For all tasks with different available channels, our model achieves outstanding performance not only on healthy subjects but even on patients with sleep disorders (SHHS: Acc-0.87, K-0.81; ISRUC: Acc-0.86, K-0.82; Sleep-EDF: Acc-0.86, K-0.81). The highest classification accuracy is achieved by a fusion of multiple polysomnographic signals.
Comparison: Compared to state-of-the-art methods that use the same dataset, the proposed model achieves a comparable or better performance, and exhibits low computational cost.
Conclusions: The model demonstrates its transferability among different datasets, without changing model architecture or hyper-parameters across tasks. Good model transferability promotes the application of transfer learning on small group studies with mismatched channels. Due to demonstrated availability and versatility, the proposed method can be integrated with diverse polysomnography systems, thereby facilitating sleep monitoring in clinical or routine care.

Keywordssleepsleep researchsignal analysissignal processingmachine learning

Free keywordspolysomnography; automatic sleep scoring; multi-modality analysis; deep learning

Contributing organizations

Ministry reportingYes

Reporting Year2021

JUFO rating1

Last updated on 2024-22-04 at 21:24