A4 Article in conference proceedings
LightSleepNet : A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms (2021)

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

JYU authors or editors

Publication details

All authors or editors: Zhou, Dongdong; Xu, Qi; Wang, Jian; Zhang, Jiacheng; Hu, Guoqiang; Kettunen, Lauri; Chang, Zheng; Cong, Fengyu

Parent publication: EMBC 2021 : 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Place and date of conference: Mexico, 1.-5.11.2021

ISBN: 978-1-7281-1180-3

eISBN: 978-1-7281-1179-7

Journal or series: Annual International Conference of the IEEE Engineering in Medicine and Biology Society

ISSN: 2375-7477

eISSN: 1557-170X

Publication year: 2021

Publication date: 01/11/2021

Pages range: 43-46

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/embc46164.2021.9629878

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/85535

Abstract

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.

Keywords: sleep research; modelling (representation); EEG; signal processing; deep learning; neural networks (information technology)

Free keywords: deep learning; training; power demand; sleep; computational modeling; biological system modeling; brain modeling

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2021

JUFO rating: 1