TY - GEN
T1 - Sleep Dynamic Analysis Technology Based on Cross-Phase-Amplitude Transfer Entropy in Multiple Brain Regions
AU - Wang, Yufei
AU - Shi, Wenbin
AU - Yeh, Chien Hung
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Information flow existed across brain regions, and varies dynamically during sleep. In evaluating brain communication and neural-oscillation connectivity across spatiotemporal scales, the phase-amplitude coupling (PAC) is well-explored. However, the directional connectivity is still a deficiency. In this work, we propose a cross-phase-amplitude transfer entropy method in quantifying the characteristics of multi-regional sleep dynamics. The simulation of multivariate nonlinear and nonstationary signals verifies both effectiveness and veracity of the proposed algorithm. The results achieved in sleep EEG of healthy adults indicate that the direction of PAC is from the occipital lobe to the frontal lobe in the Awake and N1 sleep stages. And the flow of PAC turns to the opposite direction for the other sleep stages, i.e., frontal-to-occipital lobe. Besides, the PAC gradually strengthens with the deepening of the sleep. Of note, the PAC results in the REM sleep stage vary across different frequency pairs. The obtained results support the proposed method as a reliable tool in evaluating brain functions during sleep with brain signals. Clinical Relevance - This manifests the brain communication and neuron-oscillation connectivity across spatiotemporal scales. The proposed framework may be useful in identifying multi-regional sleep dynamics.
AB - Information flow existed across brain regions, and varies dynamically during sleep. In evaluating brain communication and neural-oscillation connectivity across spatiotemporal scales, the phase-amplitude coupling (PAC) is well-explored. However, the directional connectivity is still a deficiency. In this work, we propose a cross-phase-amplitude transfer entropy method in quantifying the characteristics of multi-regional sleep dynamics. The simulation of multivariate nonlinear and nonstationary signals verifies both effectiveness and veracity of the proposed algorithm. The results achieved in sleep EEG of healthy adults indicate that the direction of PAC is from the occipital lobe to the frontal lobe in the Awake and N1 sleep stages. And the flow of PAC turns to the opposite direction for the other sleep stages, i.e., frontal-to-occipital lobe. Besides, the PAC gradually strengthens with the deepening of the sleep. Of note, the PAC results in the REM sleep stage vary across different frequency pairs. The obtained results support the proposed method as a reliable tool in evaluating brain functions during sleep with brain signals. Clinical Relevance - This manifests the brain communication and neuron-oscillation connectivity across spatiotemporal scales. The proposed framework may be useful in identifying multi-regional sleep dynamics.
UR - http://www.scopus.com/inward/record.url?scp=85138127827&partnerID=8YFLogxK
U2 - 10.1109/EMBC48229.2022.9871136
DO - 10.1109/EMBC48229.2022.9871136
M3 - Conference contribution
C2 - 36086398
AN - SCOPUS:85138127827
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2953
EP - 2956
BT - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Y2 - 11 July 2022 through 15 July 2022
ER -