Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features

2016 ◽  
Vol 63 (6) ◽  
pp. 1321-1332 ◽  
2013 ◽  
Vol 46 (31) ◽  
pp. 249-254 ◽  
Author(s):  
Hirotsugu Hashimoto ◽  
Koichi Fujiwara ◽  
Yoko Suzuki ◽  
Miho Miyajima ◽  
Toshitaka Yamakawa ◽  
...  

2000 ◽  
Vol 24 (2-7) ◽  
pp. 291-296 ◽  
Author(s):  
B. Lennox ◽  
H.G. Hiden ◽  
G.A. Montague ◽  
G. Kornfeld ◽  
P.R. Goulding

AIChE Journal ◽  
2010 ◽  
Vol 57 (9) ◽  
pp. 2360-2368 ◽  
Author(s):  
Bundit Boonkhao ◽  
Rui F. Li ◽  
Xue Z. Wang ◽  
Richard J. Tweedie ◽  
Ken Primrose

2017 ◽  
Vol 2 (2) ◽  
pp. 1 ◽  
Author(s):  
Jing Jiang ◽  
Hua-Ming Song

In this paper, we propose an ensemble method based on bagging and decision tree to resolve the problem of diagnosing out-of-control signals in multivariate statistical process control. To classify the out-of-control signals, we obtain a series of classifiers through ensemble learning on decision tree. Then we will integrate the classification results of multiple classifiers to determine the final classification. The experimental results show that our method could improve the accuracy of classification and is superior to other methods in terms of diagnosing out-of-control signals in multivariate statistical process control.


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