A4 Artikkeli konferenssijulkaisussa
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-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Zhou, Dongdong; Xu, Qi; Wang, Jian; Zhang, Jiacheng; Hu, Guoqiang; Kettunen, Lauri; Chang, Zheng; Cong, Fengyu
Emojulkaisu: EMBC 2021 : 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Konferenssin paikka ja aika: Mexico, 1.-5.11.2021
ISBN: 978-1-7281-1180-3
eISBN: 978-1-7281-1179-7
Lehti tai sarja: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
ISSN: 2375-7477
eISSN: 1557-170X
Julkaisuvuosi: 2021
Ilmestymispäivä: 01.11.2021
Artikkelin sivunumerot: 43-46
Kustantaja: IEEE
Julkaisumaa: Yhdysvallat (USA)
Julkaisun kieli: englanti
DOI: https://doi.org/10.1109/embc46164.2021.9629878
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/85535
Tiivistelmä
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.
YSO-asiasanat: unitutkimus; mallintaminen; EEG; signaalinkäsittely; syväoppiminen; neuroverkot
Vapaat asiasanat: deep learning; training; power demand; sleep; computational modeling; biological system modeling; brain modeling
Liittyvät organisaatiot
OKM-raportointi: Kyllä
VIRTA-lähetysvuosi: 2021
JUFO-taso: 1
