TY - GEN
T1 - ENHANCING CONTEXTUAL ENCODING WITH STAGE-CONFUSION AND STAGE-TRANSITION ESTIMATION FOR EEG-BASED SLEEP STAGING
AU - Phyo, Jaeun
AU - Ko, Wonjun
AU - Jeon, Eunjin
AU - Suk, Heung
N1 - Funding Information:
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government under Grant 2017-0-00451 (Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning) and Grant 2019-0-00079 (Department of Artificial Intelligence, Korea University). †: corresponding author
Publisher Copyright:
© 2022 IEEE
PY - 2022
Y1 - 2022
N2 - Sleep staging is essential for sleep assessment and plays a vital role as one of the health indicators. It is challenging to correctly classify stage-transitioning epochs of sleep electroencephalography (EEG) because of their mixed signals of stages. To this end, recent studies exploited and devised various deep learning architectures. However, those are still suffering from confusing two or more stages, especially in stage-transitioning epochs. In this work, we propose a novel network architecture that takes advantage of two auxiliary classification tasks and exploits their outputs to adapt feature representations, thus effectively discriminating confusing stages. Specifically, one auxiliary task is an epoch-level stage classification to produce confidence scores about stages. The other is a stage-transition detection to learn inter-epoch relations. Using inferred information about stage-confusion at an epoch level and stage-transition across neighboring epochs helps learn more concrete representations for stage identification. We demonstrated and analyzed the validity of our proposed method over two publicly available datasets, achieving promising performances.
AB - Sleep staging is essential for sleep assessment and plays a vital role as one of the health indicators. It is challenging to correctly classify stage-transitioning epochs of sleep electroencephalography (EEG) because of their mixed signals of stages. To this end, recent studies exploited and devised various deep learning architectures. However, those are still suffering from confusing two or more stages, especially in stage-transitioning epochs. In this work, we propose a novel network architecture that takes advantage of two auxiliary classification tasks and exploits their outputs to adapt feature representations, thus effectively discriminating confusing stages. Specifically, one auxiliary task is an epoch-level stage classification to produce confidence scores about stages. The other is a stage-transition detection to learn inter-epoch relations. Using inferred information about stage-confusion at an epoch level and stage-transition across neighboring epochs helps learn more concrete representations for stage identification. We demonstrated and analyzed the validity of our proposed method over two publicly available datasets, achieving promising performances.
KW - deep learning
KW - electroencephalography
KW - sequence-to-sequence
KW - sleep staging
UR - http://www.scopus.com/inward/record.url?scp=85131254435&partnerID=8YFLogxK
U2 - 10.1109/ICASSP43922.2022.9746353
DO - 10.1109/ICASSP43922.2022.9746353
M3 - Conference contribution
AN - SCOPUS:85131254435
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1301
EP - 1305
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -