Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Classification of sleep stages in infants: a neuro fuzzy approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Heiss, J.E. ; Dept. of Electr. Eng., Chile Univ., Santiago, Chile ; Held, C.M. ; Estevez, P.A. ; Perez, C.A.
more authors

An ANFIS based neuro-fuzzy classifier with a pruning algorithm was implemented and applied to the classification of sleep-waking states-stages in infants, using the sleep pattern detection system of P.A. Estevez (2002) to generate the inputs. Including artifacted pages, an average of 88.2% of expert agreement was achieved for testing data. As a result of the training process and pruning, rules and parameters that defined a fuzzy classification system were also determined. Analyzing the rules obtained for sleep-stage NREM-1, it was found that the main rule matched the expert rule to classify NREM-1. Additional rules were discovered that complement the classification and may provide additional information about the characteristics of this sleep stage. This is a promissory result, and further research is needed in this topic. Future work includes implementation of a clustering algorithm to determine the initial parameters of the system, training the system with a different objective function, such as the max-type error function described in J.S.R. Jang and C.T. Sun (1993), and evaluating the performance of different T-norms at layer 2 in Figure 1. The development of a general methodology for rule discovery and interpretation is also of interest.

Published in:

Engineering in Medicine and Biology Magazine, IEEE  (Volume:21 ,  Issue: 5 )