A4 Artikkeli konferenssijulkaisussa
A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series (2020)


Yan, Rui; Li, Fan; Zhou, DongDong; Ristaniemi, Tapani; Cong, Fengyu (2020). A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series. In EUSIPCO 2020 : 28th European Signal Processing Conference (pp. 1090-1094). European Signal Processing Conference. IEEE. DOI: 10.23919/Eusipco47968.2020.9287518


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Yan, Rui; Li, Fan; Zhou, DongDong; Ristaniemi, Tapani; Cong, Fengyu

Emojulkaisu: EUSIPCO 2020 : 28th European Signal Processing Conference

Konferenssin paikka ja aika: Amsterdam, Netherlands, 18.-21.1.2021

ISBN: 978-1-7281-5001-7

eISBN: 978-9-0827-9705-3

Lehti tai sarja: European Signal Processing Conference

ISSN: 2219-5491

eISSN: 2076-1465

Julkaisuvuosi: 2020

Artikkelin sivunumerot: 1090-1094

Kustantaja: IEEE

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.23919/Eusipco47968.2020.9287518

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Julkaisun avoin saatavuus:

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/73485


Tiivistelmä

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.


YSO-asiasanat: aivotutkimus; unitutkimus; uni (lepotila); signaalianalyysi; signaalinkäsittely; aikasarjat; koneoppiminen

Vapaat asiasanat: polysomnography; automatic sleep scoring; multimodality analysis; deep learning; transfer learning


Liittyvät organisaatiot


OKM-raportointi: Kyllä

Alustava JUFO-taso: 1


Viimeisin päivitys 2020-29-12 klo 09:40