A4 Artikkeli konferenssijulkaisussa
A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series (2020)
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
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
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan 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
Raportointivuosi: 2020
JUFO-taso: 1