Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Wenpo Yao"'
Publikováno v:
Physiol. Meas. 44 095004 (2023)
Objective. The distribution of equal states (DES) quantifies amplitude fluctuations in biomedical signals. However, under certain conditions, such as a high resolution of data collection or special signal processing techniques, equal states may be ve
Externí odkaz:
http://arxiv.org/abs/2311.05847
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 2006-2017 (2023)
Schizophrenia is a serious mental disorder. Network analysis of magnetoencephalogram signals may help to identify potential biomarkers of schizophrenia. The goal of this investigation was to identify potential biomarkers in the magnetoencephalogram s
Externí odkaz:
https://doaj.org/article/360f1fc8ceb14bf591328046a52d7043
Autor:
Yao, Wenpo Yao Wenli, Wang, Jun
Symbolic relative entropy, an efficient nonlinear complexity parameter measuring probabilistic divergences of symbolic sequences, is proposed in our nonlinear dynamics analysis of heart rates considering equal states. Equalities are not rare in discr
Externí odkaz:
http://arxiv.org/abs/1801.02665
Publikováno v:
AIP Advances, Vol 13, Iss 3, Pp 035234-035234-8 (2023)
In this study, we employ the Granger causality of a polynomial kernel to identify the coupling causality of depressed magnetoencephalography (MEG). We collect MEG under positive, neutral, and negative emotional stimuli and focus on the β-band activi
Externí odkaz:
https://doaj.org/article/26ba9441fe6642d38370a32347f4c887
Publikováno v:
Entropy, Vol 25, Iss 7, p 1006 (2023)
Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct d
Externí odkaz:
https://doaj.org/article/6f3f814ac4164d578f4f1be1fa7c62f6
Publikováno v:
Entropy, Vol 24, Iss 3, p 314 (2022)
Schizophrenia is a neuropsychiatric disease that affects the nonlinear dynamics of brain activity. The primary objective of this study was to explore the complexity of magnetoencephalograms (MEG) in patients with schizophrenia. We combined a multisca
Externí odkaz:
https://doaj.org/article/48a806a7284d4e20803fbdfb6ec97aac
Publikováno v:
AIP Advances, Vol 9, Iss 2, Pp 029902-029902-1 (2019)
Externí odkaz:
https://doaj.org/article/026b357c1b5d4d82ac9dc034d7487c1d
Publikováno v:
Entropy; Volume 25; Issue 7; Pages: 1006
Network analysis is an important approach to explore complex brain structures under different pathological and physiological conditions. In this paper, we employ the multivariate inhomogeneous polynomial kernel Granger causality (MKGC) to construct d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::107058a6b5f110efabccade6dd9ea1a2
Publikováno v:
2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e3c15ec62e0d6dfe76fc48cb24bb046b
https://doi.org/10.1109/cisp-bmei56279.2022.9980315
https://doi.org/10.1109/cisp-bmei56279.2022.9980315
Publikováno v:
AIP Advances, Vol 7, Iss 7, Pp 075313-075313-9 (2017)
Symbolizations, the base of symbolic dynamic analysis, are classified as global static and local dynamic approaches which are combined by joint entropy in our works for nonlinear dynamic complexity analysis. Two global static methods, symbolic transf
Externí odkaz:
https://doaj.org/article/f2d5086af66f49d7b8277587144e6d2b