A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series
Yan, R., Li, F., Zhou, D., Ristaniemi, T., & Cong, F. (2020). A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series. In EUSIPCO 2020 : 28th European Signal Processing Conference (pp. 1090-1094). IEEE. European Signal Processing Conference. https://doi.org/10.23919/Eusipco47968.2020.9287518
Published inEuropean Signal Processing Conference
Yan, Rui |
Li, Fan |
DisciplineTietotekniikkaMonitieteinen aivotutkimuskeskusMathematical Information TechnologyCentre for Interdisciplinary Brain Research
© IEEE, 2020
Sleep scoring is a fundamental but time-consuming process in any sleep laboratory. Automatic sleep scoring is crucial and urgent to help address the increasing unmet need for sleep research. Therefore, this paper aims to develop an end-to-end deep learning architecture using raw polysomnographic recordings to automate sleep scoring. The proposed model adopts two-dimensional convolutional neural networks (2D-CNN) to automatically learn features from multi-modality signals, together with a "squeeze and excitation" block for recalibrating channel-wise feature responses. The learnt representations are finally fed to a softmax classifier to generate predictions for each sleep stage. The model performance is evaluated on two public sleep datasets (SHHS and Sleep-EDF) with different available channels. The results have shown that our model achieves an overall accuracy of 85.2% on the SHHS dataset and an accuracy of 85% on the Sleep-EDF dataset. We have also demonstrated that the proposed architecture not only is able to handle various numbers of input channels and several signal modalities from different datasets but also exhibits short runtimes and low computational cost. ...
Parent publication ISBN978-1-7281-5001-7
Is part of publicationEUSIPCO 2020 : 28th European Signal Processing Conference
ISSN Search the Publication Forum2219-5491
Publication in research information system
MetadataShow full item record
Additional information about fundingThis work is supported by the scholarship from China Scholarship Council (Nos. 201606060227).
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