LightSleepNet : A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms
Zhou, D., Xu, Q., Wang, J., Zhang, J., Hu, G., Kettunen, L., Chang, Z., & Cong, F. (2021). LightSleepNet : A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms. In EMBC 2021 : 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 43-46). IEEE. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. https://doi.org/10.1109/embc46164.2021.9629878
Julkaistu sarjassa
Annual International Conference of the IEEE Engineering in Medicine and Biology SocietyTekijät
Xu, Qi |
Päivämäärä
2021Oppiaine
TekniikkaTietotekniikkaComputing, Information Technology and MathematicsSecure Communications Engineering and Signal ProcessingLaskennallinen tiedeEngineeringMathematical Information TechnologyComputing, Information Technology and MathematicsSecure Communications Engineering and Signal ProcessingComputational ScienceTekijänoikeudet
© 2021, IEEE
Deep learning has achieved unprecedented success in sleep stage classification tasks, which starts to pave the way for potential real-world applications. However, due to its enormous size, deployment of deep neural networks is hindered by high cost at various aspects, such as computation power, storage, network bandwidth, power consumption, and hardware complexity. For further practical applications (e.g., wearable sleep monitoring devices), there is a need for simple and compact models. In this paper, we propose a lightweight model, namely LightSleepNet, for rapid sleep stage classification based on spectrograms. Our model is assembled by a much fewer number of model parameters compared to existing ones. Furthermore, we convert the raw EEG data into spectrograms to speed up the training process. We evaluate the model performance on several public sleep datasets with different characteristics. Experimental results show that our lightweight model using spectrogram as input can achieve comparable overall accuracy and Cohen’s kappa (SHHS100: 86.7%-81.3%, Sleep-EDF: 83.7%-77.5%, Sleep-EDF-v1: 88.3%-84.5%) compared to the state-of-the-art methods on experimental datasets.
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Julkaisija
IEEEEmojulkaisun ISBN
978-1-7281-1180-3Konferenssi
Kuuluu julkaisuun
EMBC 2021 : 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology SocietyISSN Hae Julkaisufoorumista
2375-7477Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/102379324
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Lisätietoja rahoituksesta
This work was support by National Natural Science Foundation of China (Grant No.91748105), National Foundation in China (No. JCKY2019110B009, 2020-JCJQ-JJ-252), Fundamental Research Funds for Central Universities [DUT2019, DUT20LAB303] in Dalian University of Technology in China and the scholarships from China Scholarship Council (No.201806060164, No.202006060226), CAAI-Huawei MindSpore Open Fund (CAAIXSJLJJ-2020-024A). ...Lisenssi
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