Probabilistic network values

Author(s):  
Surajit Borkotokey ◽  
Subhadip Chakrabarti ◽  
Robert P. Gilles ◽  
Loyimee Gogoi ◽  
Rajnish Kumar
1997 ◽  
Vol 36 (04/05) ◽  
pp. 41-46
Author(s):  
A. Kjaer ◽  
W. Jensen ◽  
T. Dyrby ◽  
L. Andreasen ◽  
J. Andersen ◽  
...  

Abstract.A new method for sleep-stage classification using a causal probabilistic network as automatic classifier has been implemented and validated. The system uses features from the primary sleep signals from the brain (EEG) and the eyes (AOG) as input. From the EEG, features are derived containing spectral information which is used to classify power in the classical spectral bands, sleep spindles and K-complexes. From AOG, information on rapid eye movements is derived. Features are extracted every 2 seconds. The CPN-based sleep classifier was implemented using the HUGIN system, an application tool to handle causal probabilistic networks. The results obtained using different training approaches show agreements ranging from 68.7 to 70.7% between the system and the two experts when a pooled agreement is computed over the six subjects. As a comparison, the interrater agreement between the two experts was found to be 71.4%, measured also over the six subjects.


2015 ◽  
Vol 23 ◽  
pp. 90-94 ◽  
Author(s):  
Hui Yang ◽  
Lei Cheng ◽  
Guangjun Luo ◽  
Jie Zhang ◽  
Yongli Zhao ◽  
...  

2017 ◽  
Vol 66 (11) ◽  
pp. 10406-10418
Author(s):  
Bin Cao ◽  
Li Qiao ◽  
Yun Li ◽  
Chonggang Wang ◽  
Hsiao-Hwa Chen

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